D-Wave Hybrid#
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!
Example#
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))
Documentation#
- Release:
8.0.1
- Date:
Sep 20, 2024
Note
This documentation is for the latest version of dwave-hybrid. Documentation for the version currently installed by dwave-ocean-sdk is here: dwave-hybrid.
Code
Ocean Software