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!
Install from a package on PyPI:
pip install dwave-hybridor from source:
git clone https://github.com/dwavesystems/dwave-hybrid.git
cd dwave-hybrid
pip install -r requirements.txt
python setup.py installimport dimod
from hybrid.samplers import (
QPUSubproblemAutoEmbeddingSampler, InterruptableTabuSampler)
from hybrid.decomposers import EnergyImpactDecomposer
from hybrid.composers import SplatComposer
from hybrid.core import State
from hybrid.flow import RacingBranches, ArgMin, Loop
from hybrid.utils import min_sample
# Construct a problem
bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)
# Define the solver
iteration = RacingBranches(
InterruptableTabuSampler(),
EnergyImpactDecomposer(max_size=2)
| QPUSubproblemAutoEmbeddingSampler()
| SplatComposer()
) | ArgMin()
main = Loop(iteration, max_iter=10, convergence=3)
# Solve the problem
init_state = State.from_sample(min_sample(bqm), bqm)
solution = main.run(init_state).result()
# Print results
print("Solution: sample={s.samples.first}".format(s=solution))Released under the Apache License 2.0. See LICENSE file.