def workflow_runner(bqm,
factory,
energy_threshold=None,
qpu=None,
**kwargs):
workflow = factory(qpu=qpu, energy_threshold=energy_threshold)
init_state = hybrid.State.from_sample(hybrid.min_sample(bqm), bqm)
with hybrid.tictoc() as timer:
samples = workflow.run(init_state).result().samples
return samples, timer
def sampler_runner(bqm,
factory,
energy_threshold=None,
qpu=None,
**kwargs):
sampler = factory(init_sample=lambda: hybrid.min_sample(bqm),
energy_threshold=energy_threshold,
qpu=qpu)
with hybrid.tictoc() as timer:
samples = sampler(bqm)
return samples, timer
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
init_state = hybrid.State.from_sample(hybrid.min_sample(bqm), bqm)
final_state = global_update.run(init_state).result()
# show execution profile
hybrid.profiling.print_counters(global_update)
# show results
print("Solution: sample={.samples.first}".format(tracker.best))
