print("BQM size: {}, subproblem size: {}".format(len(bqm), subproblem_size))
# Classical solvers
#subproblem = hybrid.EnergyImpactDecomposer(size=50, rolling_history=0.15)
#subproblem = hybrid.EnergyImpactDecomposer(size=1024, rolling_history=0.15, traversal="bfs")
# Parallel subproblem
subproblem = hybrid.Unwind(
#hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.15, traversal="bfs")
hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.1))
# QPU
#subsampler = hybrid.QPUSubproblemAutoEmbeddingSampler() | hybrid.SplatComposer()
subsampler = hybrid.Map(
hybrid.QPUSubproblemAutoEmbeddingSampler()) | hybrid.Reduce(
hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()
# Define the workflow
# iteration = hybrid.RacingBranches(
# hybrid.InterruptableTabuSampler(),
# hybrid.EnergyImpactDecomposer(size=2)
# | hybrid.QPUSubproblemAutoEmbeddingSampler()
# | hybrid.SplatComposer()
# ) | hybrid.ArgMin()
#iteration = hybrid.RacingBranches(
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
#hybrid.SimulatedAnnealingProblemSampler(),
subproblem | subsampler) | hybrid.ArgMin()
# define the solver
def merge_substates(component, substates):
a, b = substates
return a.updated(
subsamples=hybrid.utils.meld_samplesets(a.subsamples, b.subsamples)
)
subproblems = hybrid.Unwind(
hybrid.EnergyImpactDecomposer(size=50, silent_rewind=False, rolling_history=0.15)
)
qpu = hybrid.Map(
hybrid.QPUSubproblemAutoEmbeddingSampler()
) | hybrid.Reduce(
hybrid.Lambda(merge_substates)
) | hybrid.SplatComposer()
random = hybrid.Map(
hybrid.RandomSubproblemSampler()
) | hybrid.Reduce(
hybrid.Lambda(merge_substates)
) | hybrid.SplatComposer()
subsampler = hybrid.Parallel(qpu, random, endomorphic=False) | hybrid.ArgMin()
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
subproblems | subsampler
) | hybrid.ArgMin()