# number of generations, or temperatures to progress through
num_iter = 20
# population size
num_samples = 20
# QPU initial sampling: limits the PA workflow to QPU-sized problems
qpu_init = (hybrid.IdentityDecomposer()
| hybrid.QPUSubproblemAutoEmbeddingSampler(num_reads=num_samples)
| hybrid.IdentityComposer()) | hybrid.AggregatedSamples(False)
# PA workflow: after initial beta schedule estimation, we do `num_iter` steps
# (one per beta/temperature) of fixed-temperature sampling / weighted resampling
workflow = qpu_init | CalculateAnnealingBetaSchedule(
length=num_iter) | hybrid.Loop(
ProgressBetaAlongSchedule() | FixedTemperatureSampler(
num_sweeps=num_sweeps) | EnergyWeightedResampler(),
max_iter=num_iter)
# run the workflow
state = hybrid.State.from_problem(bqm)
solution = workflow.run(state).result()
# show execution profile
hybrid.profiling.print_counters(workflow)
# show results
print("Solution: sample={0.samples.first}, energy={0.samples.first.energy}".
format(solution))
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)
for beta in betas[1:]
])
# swap step is `n_replicas-1` pairwise potential swaps
swap = SwapReplicasDownsweep(betas=betas)
# we'll run update/swap sequence for `n_iterations`
workflow = hybrid.Loop(update | swap, max_iter=n_iterations) \
| hybrid.MergeSamples(aggregate=True)
# execute the workflow
solution = workflow.run(replicas).result()
# show execution profile
hybrid.profiling.print_counters(workflow)
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}".
format(solution))
print("BQM: {} nodes, {} edges, {:.2f} density".format(
len(bqm), len(bqm.quadratic), hybrid.bqm_density(bqm)))
# sweeps per fixed-temperature sampling step
num_sweeps = 1000
# number of generations, or temperatures to progress through
num_iter = 20
# population size
num_samples = 20
# PA workflow: after initial beta schedule estimation, we do `num_iter` steps
# (one per beta/temperature) of fixed-temperature sampling / weighted resampling
workflow = CalculateAnnealingBetaSchedule(length=num_iter) | hybrid.Loop(
ProgressBetaAlongSchedule()
| FixedTemperatureSampler(num_sweeps=num_sweeps, num_reads=num_samples)
| EnergyWeightedResampler(),
max_iter=num_iter)
# run the workflow
state = hybrid.State.from_problem(bqm)
solution = workflow.run(state).result()
# show execution profile
hybrid.profiling.print_counters(workflow)
# show results
print("Solution: sample={0.samples.first}, energy={0.samples.first.energy}".
format(solution))