azure.quantum.optimization.md

author	description	ms.author	ms.date	ms.service	ms.subservice	ms.topic	title	uid
KittyYeungQ	Reference for azure.quantum.optimization	kitty	02/01/2021	azure-quantum	optimization	reference	Azure.Quantum.Optimization	microsoft.quantum.reference.python-sdk.azure.quantum.optimization

Azure.Quantum.Optimization

from azure.quantum.optimization import Job

Job.get_results

Retrieves the job result (that is, the computed solution and energy). If the job has not yet finished, blocks until it has.

Job.has_completed

Returns a boolean value indicating whether the job has finished (for example, the job is in a final state).

job = workspace.get_job(jobId)
job.refresh()
print(job.has_completed())

> False

Job.refresh

Refreshes the job's details by querying the Workspace.

job = workspace.get_job(jobId)
job.refresh()
print(job.id)

> 5d2f9cd70f55f149e3ed3aef

Job.wait_until_completed

Keeps refreshing the job's details until it reaches a final state. For more information on job states, see Azure Quantum Overview.

job = workspace.get_job(jobId)
job.wait_until_completed()
print(job.has_completed())

> True

Problem

from azure.quantum.optimization import Problem

Constructor

To create a Problem object, you must specify the following information:

• name: A friendly name for your problem. No uniqueness constraints.
• [optional] terms: A list of Term objects to add to the problem.
• [optional] problem_type: The type of problem. Must be either ProblemType.ising or ProblemType.pubo

Problem.add_term

Adds a single term to the problem. It takes a weight for the term and the indices of variables that appear in the term.

weight = 0.13
problem.add_term(w=5, indices=[2,0])

Problem.add_terms

Adds multiple terms to the problem using a list of Terms.

problem.add_terms([
    Term(w=-9, indices=[0]),
    Term(w=-3, indices=[1,0]),
    Term(w=5, indices=[2,0]),
    Term(w=9, indices=[2,1]),
    Term(w=2, indices=[3,0]),
    Term(w=-4, indices=[3,1]),
    Term(w=4, indices=[3,2])
])

Problem.serialize

Serializes a problem to a json string.

import json

# Create a problem definition
problem = Problem("My Problem", [Term(w=1, indices=[0,1])])

# Save problem definition to a .json file
with open('myfile.json', 'w', encoding ='utf8') as json_file: 
    json_file.write(problem.serialize())
    
# Open json file
with open('myfile.json', 'r', encoding ='utf8') as json_file: 
    problem = json.load(json_file)
    print("Version: " + problem["cost_function"]["version"])
    print("Type: " + problem["cost_function"]["type"])
    print("Terms: " + str(problem["cost_function"]["terms"]))

Problem.upload

Problem data can be explicitly uploaded to an Azure storage account using its upload method that receives as a parameter the Workspace instance:

problem.upload(workspace=workspace)

Once a problem is explicitly uploaded, it will not be automatically uploaded during submission unless its terms change.

ProblemType

from azure.quantum.optimization import ProblemType

The ProblemType enum allows you to specify the type of optimization problem you would like to solve.

ProblemType.pubo

A polynomial unconstrained binary optimization problem (PUBO) is a problem of the form:

$$H = \sum_{i} c_{i} x_{i} + \sum_{i,j} c_{i,j} x_{i} x_{j} + \sum_{i,j,k} c_{i,j,k} x_{i} x_{j} x_{k} \text{ where } c_{i,j,k} \in R \text{ and } x_{i,j,k} \in [0, 1]$$

Where H is the cost function, also known as the Hamiltonian. It is called k-local if the maximum degree of the polynomial is k.

ProblemType.ising

An Ising Model is a cost function of the form:

$$H = \sum_{i} c_{i} s_{i} + \sum_{i,j} c_{i,j} s_{i} s_{j} + \sum_{i,j,k} c_{i,j,k} s_{i} s_{j} s_{k} \text{ where } c_{i,j,k} \in R \text{ and } s_{i,j,k} \in [-1, 1]$$

It is called k-local if the maximum degree of the polynomial is k.