Metadata-Version: 2.1
Name: dwave-hybrid
Version: 0.6.11
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
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Provides-Extra: test
License-File: LICENSE

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    :alt: Latest version on PyPI

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    :target: https://pypi.org/project/dwave-hybrid/
    :alt: Supported Python versions


=============
D-Wave Hybrid
=============

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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!

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Installation or Building
========================

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Install from a package on PyPI::

    pip install dwave-hybrid

or from source in development mode::

    git clone https://github.com/dwavesystems/dwave-hybrid.git
    cd dwave-hybrid
    pip install -e .

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Testing
=======

Install test requirements and run ``unittest``::

    pip install -r tests/requirements.txt
    python -m unittest


Example
=======

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.. 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))


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Documentation
=============

Documentation for latest stable release included in Ocean is available as part
of `Ocean docs <https://docs.ocean.dwavesys.com/en/stable/docs_hybrid/>`_.

License
=======

Released under the Apache License 2.0. See `<LICENSE>`_ file.

Contributing
============

Ocean's `contributing guide <https://docs.ocean.dwavesys.com/en/stable/contributing.html>`_
has guidelines for contributing to Ocean packages.
