from __future__ import print_function
import sys
import dimod
import hybrid
# load a problem
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# construct a Dialectic Search workflow
generate_antithesis = (hybrid.IdentityDecomposer()
| hybrid.RandomSubproblemSampler()
| hybrid.SplatComposer()
| hybrid.TabuProblemSampler())
generate_synthesis = (hybrid.GreedyPathMerge() | hybrid.TabuProblemSampler())
tracker = hybrid.TrackMin()
local_update = hybrid.LoopWhileNoImprovement(
hybrid.Parallel(hybrid.Identity(), generate_antithesis)
| generate_synthesis | tracker,
max_tries=10)
global_update = hybrid.Loop(generate_antithesis | local_update, max_iter=10)
# run the workflow
# define a qbsolv-like workflow
def merge_substates(_, substates):
a, b = substates
return a.updated(
subsamples=hybrid.hstack_samplesets(a.subsamples, b.subsamples))
subproblems = hybrid.Unwind(
hybrid.EnergyImpactDecomposer(size=50, 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()
main = hybrid.Loop(iteration, max_iter=10, convergence=3)
# run the workflow
init_state = hybrid.State.from_sample(hybrid.min_sample(bqm), bqm)
solution = main.run(init_state).result()
# show execution profile
hybrid.profiling.print_counters(main)