Metadata-Version: 2.1
Name: dwave-hybrid
Version: 0.4.3
Summary: Hybrid Asynchronous Decomposition Solver Framework
Home-page: https://github.com/dwavesystems/dwave-hybrid
Author: D-Wave Systems Inc.
Author-email: radomir@dwavesys.com
License: Apache 2.0
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        =============
        D-Wave Hybrid
        =============
        
        .. index-start-marker
        
        A general, minimal Python framework for building hybrid asynchronous decomposition
        samplers for quadratic unconstrained binary optimization (QUBO) problems.
        
        *dwave-hybrid* facilitates three aspects of solution development:
        
        *   Hybrid approaches to combining quantum and classical compute resources
        *   Evaluating a portfolio of algorithmic components and problem-decomposition strategies
        *   Experimenting with workflow structures and parameters to obtain the best application results
        
        The framework enables rapid development and insight into expected performance
        of productized versions of its experimental prototypes.
        
        Your optimized algorithmic components and other contributions to this project are welcome!
        
        .. index-end-marker
        
        
        Installation or Building
        ========================
        
        .. installation-start-marker
        
        Install from a package on PyPI::
        
            pip install dwave-hybrid
        
        or from source::
        
            git clone https://github.com/dwavesystems/dwave-hybrid.git
            cd dwave-hybrid
            pip install -r requirements.txt
            python setup.py install
        
        .. installation-end-marker
        
        
        Example
        =======
        
        .. example-start-marker
        
        .. code-block:: python
        
            import dimod
            import hybrid
        
            # Construct a problem
            bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)
        
            # Define the workflow
            iteration = hybrid.RacingBranches(
                hybrid.InterruptableTabuSampler(),
                hybrid.EnergyImpactDecomposer(size=2)
                | hybrid.QPUSubproblemAutoEmbeddingSampler()
                | hybrid.SplatComposer()
            ) | hybrid.ArgMin()
            workflow = hybrid.LoopUntilNoImprovement(iteration, convergence=3)
        
            # Solve the problem
            init_state = hybrid.State.from_problem(bqm)
            final_state = workflow.run(init_state).result()
        
            # Print results
            print("Solution: sample={.samples.first}".format(final_state))
        
        
        .. example-end-marker
        
        
        License
        =======
        
        Released under the Apache License 2.0. See `<LICENSE>`_ file.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Provides-Extra: test
