A Julia meta-package for accessing the python libraries pymatching (for MWPM-like decoders) and ldpc (for BP-like decoders).

How to use

pymatching

The python pymatching module is immediately available:

julia> using PyQDecoders

julia> PyQDecoders.pm
Python: <module 'pymatching' from ...>

Running the example from pymatching's original Readme:

julia> using PyQDecoders: sps, np, pm

julia> H = sps.csc_matrix(
           [1 1 0 0 0;
            0 1 1 0 0;
            0 0 1 1 0;
            0 0 0 1 1])
Python:
<4x5 sparse matrix of type '<class 'numpy.int64'>'
        with 8 stored elements in Compressed Sparse Column format>

julia> weights = [4, 3, 2, 3, 4]
5-element Vector{Int64}:
 4
 3
 2
 3
 4

julia> matching = pm.Matching(H, weights=weights)
Python: <pymatching.Matching object with 4 detectors, 1 boundary node, and 5 edges>

julia> prediction = matching.decode([0, 1, 0, 1])
Python: array([0, 0, 1, 1, 0], dtype=uint8)

julia> prediction, solution_weight = matching.decode([0, 1, 0, 1], return_weight=true);

julia> prediction
Python: array([0, 0, 1, 1, 0], dtype=uint8)

julia> solution_weight
Python: 5.0

ldpc

The python ldpc module is immediately available:

julia> using PyQDecoders

julia> PyQDecoders.ldpc
Python: <module 'ldpc' from ...>

Running the example from ldpc's original Readme:

julia> using PyQDecoders: sps, np, ldpc

julia> H = ldpc.codes.rep_code(3) # parity check matrix for the length-3 repetition code
Python:
array([[1, 1, 0],
       [0, 1, 1]])

julia> n = H.shape[1] # the codeword length, same as `size(H, 2)`
Python: 3

julia> bpd = ldpc.bp_decoder(
           H, # the parity check matrix
           error_rate=0.1, # the error rate on each bit
           max_iter=n, # the maximum iteration depth for BP
           bp_method="product_sum", # BP method. The other option is `minimum_sum'
           channel_probs=[nothing] # channel probability probabilities. Will override error rate.
       )
Python: <ldpc.bp_decoder.bp_decoder object at 0x...>

julia> error = [0,1,0]
3-element Vector{Int64}:
 0
 1
 0

julia> using PythonCall: PyArray

julia> syndrome = PyArray(H)*error .% 2
2-element Vector{Int64}:
 1
 1

julia> decoding = bpd.decode(np.array(syndrome))
Python: array([0, 1, 0])