| author | description | ms.author | ms.date | ms.service | ms.subservice | ms.topic | title | uid |
|---|---|---|---|---|---|---|---|---|
KittyYeungQ |
This document provides a basic guide to applying a optimization solver in Azure Quantum using Python. |
kitty |
02/01/2021 |
azure-quantum |
optimization |
conceptual |
Apply solvers to solve optimization problems |
microsoft.quantum.optimization.apply-solver |
Once we have a Problem, we're ready to solve it by applying a solver. In this example we'll use a parameter-free version of parallel tempering. You can find documentation on this solver and the other available solvers in the Solver overview.
solver = ParallelTempering(workspace, timeout=100)For arguments, the solver takes the Workspace created previously, plus a single parameter which is the maximum amount of time (in seconds) to run the solver. Detailed documentation on parameters is available in the reference for each solver.
Note
See Use the Python SDK for details on connecting to a workspace and getting a Workspace object for it.
Solvers provide an optimize method that expects a Problem. The optimize method uploads the problem definition, submits a job to solve the problem, and polls the status until the job has completed running. Once the job has completed, it returns a JobOutput object which contains the results. See Understand Solver results for interpreting the results.
result = solver.optimize(problem)
print(result)This method will submit the problem to Azure Quantum for optimization and synchronously wait for it to be solved. You'll see output like the following in your terminal:
> {'configuration': {'0': 1, '1': 1, '2': -1, '3': 1}, 'cost': -32.0}
See Solve long running problems for solving problems asynchronously.
By default, the CPU version of a solver is used. In order to specify a different version like FPGA, specify a platform parameter as shown below.
solver = SimulatedAnnealing(workspace, timeout=100, platform=HardwarePlatform.FPGA)