.. image:: https://badge.fury.io/py/dwave-hybrid.svg
:target: https://badge.fury.io/py/dwave-hybrid
:alt: Latest version on PyPI
.. image:: https://circleci.com/gh/dwavesystems/dwave-hybrid.svg?style=shield
:target: https://circleci.com/gh/dwavesystems/dwave-hybrid
:alt: Linux/MacOS/Windows build status
.. image:: https://img.shields.io/codecov/c/github/dwavesystems/dwave-hybrid/master.svg
:target: https://codecov.io/gh/dwavesystems/dwave-hybrid
:alt: Code coverage
.. image:: https://img.shields.io/pypi/pyversions/dwave-hybrid.svg?style=flat
:target: https://pypi.org/project/dwave-hybrid/
:alt: Supported Python versions
=============
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 in development mode::
git clone https://github.com/dwavesystems/dwave-hybrid.git
cd dwave-hybrid
pip install -e .
.. installation-end-marker
Testing
=======
Install test requirements and run ``unittest``::
pip install -r tests/requirements.txt
python -m unittest
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
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.
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