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============= 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::

git clone https://github.com/dwavesystems/dwave-hybrid.git
cd dwave-hybrid
pip install -r requirements.txt
python setup.py install

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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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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.