optimization-solve-long-running-problems.md

author	description	ms.author	ms.date	ms.service	ms.subservice	ms.topic	title	uid
KittyYeungQ	This document provides a basic guide to solving long-running problems in Azure Quantum using Python.	kitty	02/01/2021	azure-quantum	optimization	how-to	Solve long-running problems	microsoft.quantum.optimization.solve-long-running-problems

Solve long running problems

In the example in Apply solvers to solve optimization problems, a problem was submitted to Azure Quantum and solved synchronously. This is convenient for certain environments, but unsuitable for others where there is a need to either submit a problem and check on it later, or submit many problems and compare the results.

Submit the problem

To submit a problem asynchronously, use the submit method on the solver. This submits a Job which is returned by the method:

solver = ParallelTempering(workspace, timeout=100, seed=11)
job = solver.submit(problem)
print(job.id)

> ea81bb40-682f-11ea-8271-c49dede60d7c

Refresh job status

After submitting the job, you can check on the status of the job by calling the refresh method. Each time refresh is called, the job metadata gets refreshed.

job.refresh()
print(job.details.status)

> Succeeded

Get the job output

Once the job is in a final state, such as Succeeded, you may download the job output using get_results:

jobId = "ea81bb40-682f-11ea-8271-c49dede60d7c"
job = workspace.get_job(jobId)
result = job.get_results()
print(result)

> {'solutions': [{'configuration': {'0': 1, '1': 1, '2': -1, '3': 1}, 'cost': -32.0}]}