optimization-understand-solver-results.md

author	description	ms.author	ms.date	ms.service	ms.subservice	ms.topic	title	uid
KittyYeungQ	This document provides a basic guide to interpreting results of optimizations problems solved in Azure Quantum using Python.	kitty	02/01/2021	azure-quantum	optimization	reference	Understand solver results	microsoft.quantum.optimization.understand-solver-results

Understand solver results

The result of a solver job is a JobOutput object which can be examined to obtain useful information.

Some of the useful properties in the result are:

1. configuration: The dictionary describes the assignment of variables, where for each key-value pair in the dictionary the key is the index of a variable and value is the value assigned to that variable by the solver.
2. cost: The optimized solution to the problem when the configuration is applied to the variables
3. parameters: Different solvers take different parameters to solve the problem. In this example, we get:
   1. all_betas: An array of the starting temperatures for the parallel tempering solver
   2. replicas: The number of runs of the solver before evaluating the best configuration from all these runs
   3. sweeps: The number of Monte Carlo steps performed in each iteration of the solver.

The example below shows how to print the result if a solver job was submitted synchronously.

result = solver.optimize(problem)
print(result)

The example below shows the asynchronous version.

job = solver.submit(problem)
result = job.get_results()
print(result)

In both cases, the result should look like the following:

> {'version': '1.0', 'configuration': {'0': 1, '1': 1, '2': -1, '3': 1}, 'cost': -32.0, 'parameters': {'all_betas': [0.1,0.5,1,2,4], 'replicas': 70, 'sweeps': 600}}