def next(self, state, **runopts):
bqm = state.problem
# get a reasonable beta range
beta_hot, beta_cold = neal.default_beta_range(bqm)
# generate betas for all branches/replicas
betas = np.geomspace(beta_hot, beta_cold, self.num_replicas)
# create num_replicas with betas spaced with geometric progression
states = hybrid.States(*[state.updated(beta=b) for b in betas])
return states
n_sweeps = 10000
n_replicas = 10
n_iterations = 10
# states are randomly initialized
state = hybrid.State.from_problem(bqm)
# get a reasonable beta range
beta_hot, beta_cold = neal.default_beta_range(bqm)
# generate betas for all branches/replicas
betas = np.geomspace(beta_hot, beta_cold, n_replicas)
# create n_replicas with geometric distribution of betas (inverse temperature)
replicas = hybrid.States(*[state.updated(beta=b) for b in betas])
# run replicas update/swap for n_iterations
# (after each update/sampling step, do n_replicas-1 swap operations)
update = hybrid.Map(FixedTemperatureSampler(num_sweeps=n_sweeps))
swap = SwapReplicasDownsweep()
workflow = hybrid.Loop(update | swap, max_iter=n_iterations) \
| hybrid.MergeSamples(aggregate=True)
solution = workflow.run(replicas).result()
# show execution profile
hybrid.profiling.print_counters(workflow)
# show results
print("Solution: sample={0.samples.first}, energy={0.samples.first.energy}".
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
print("BQM: {} nodes, {} edges, {:.2f} density".format(
len(bqm), len(bqm.quadratic), hybrid.bqm_density(bqm)))
# PT workflow: temperature/beta is a property of a branch
n_sweeps = 10000
n_replicas = 10
n_iterations = 10
# replicas are initialized with random samples
state = hybrid.State.from_problem(bqm)
replicas = hybrid.States(*[state.updated() for _ in range(n_replicas)])
# get a reasonable beta range
beta_hot, beta_cold = neal.default_beta_range(bqm)
# generate betas for all branches/replicas
betas = np.geomspace(beta_hot, beta_cold, n_replicas)
# QPU branch: limits the PT workflow to QPU-sized problems
qpu = hybrid.IdentityDecomposer() | hybrid.QPUSubproblemAutoEmbeddingSampler(
) | hybrid.IdentityComposer()
# use QPU as the hottest temperature sampler and `n_replicas-1` fixed-temperature-samplers
update = hybrid.Branches(
qpu, *[
FixedTemperatureSampler(beta=beta, num_sweeps=n_sweeps)