def HybridizedPopulationAnnealing(num_reads=20, num_iter=20, num_sweeps=1000):
"""Workflow generator for population annealing initialized with QPU samples.
Args:
num_reads (int):
Size of the population of samples.
num_iter (int):
Number of temperatures over which we iterate fixed-temperature
sampling / resampling.
num_sweeps (int):
Number of sweeps in the fixed temperature sampling step.
Returns:
Workflow (:class:`~hybrid.core.Runnable` instance).
"""
# QPU initial sampling: limits the PA workflow to QPU-sized problems
qpu_init = (hybrid.IdentityDecomposer()
| hybrid.QPUSubproblemAutoEmbeddingSampler(num_reads=num_reads)
| hybrid.IdentityComposer()) | hybrid.AggregatedSamples(False)
# PA workflow: after initial QPU sampling and 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()
| hybrid.FixedTemperatureSampler(num_sweeps=num_sweeps)
| EnergyWeightedResampler(),
max_iter=num_iter)
return workflow
def HybridizedPopulationAnnealing(num_reads=100,
num_iter=100,
num_sweeps=100,
beta_range=None):
"""Workflow generator for population annealing initialized with QPU samples.
Args:
num_reads (int):
Size of the population of samples.
num_iter (int):
Number of temperatures over which we iterate fixed-temperature
sampling / resampling.
num_sweeps (int):
Number of sweeps in the fixed temperature sampling step.
beta_range (tuple[float], optional):
A 2-tuple defining the beginning and end of the beta
schedule, where beta is the inverse temperature. Passed to
:class:`.CalculateAnnealingBetaSchedule` for linear schedule
generation.
Returns:
Workflow (:class:`~hybrid.core.Runnable` instance).
"""
# QPU initial sampling: limits the PA workflow to QPU-sized problems
qpu_init = (hybrid.IdentityDecomposer()
| hybrid.QPUSubproblemAutoEmbeddingSampler(num_reads=num_reads)
| hybrid.IdentityComposer()) | hybrid.AggregatedSamples(False)
# PA workflow: after initial QPU sampling and initial beta schedule estimation,
# we do `num_iter` steps (one per beta/temperature) of fixed-temperature
# sampling / weighted resampling
schedule_init = CalculateAnnealingBetaSchedule(length=num_iter,
beta_range=beta_range,
interpolation='linear')
workflow = qpu_init | schedule_init | hybrid.Loop(
ProgressBetaAlongSchedule()
| hybrid.FixedTemperatureSampler(num_sweeps=num_sweeps)
| EnergyWeightedResampler(),
max_iter=num_iter)
return workflow
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
# 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)
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)
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)
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, num_sweeps=n_sweeps) for beta in betas[1:]])
# swap step is `n_replicas-1` pairwise potential swaps
swap = SwapReplicasSweepDown(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()
def HybridizedParallelTempering(num_sweeps=10000,
num_replicas=10,
max_iter=None,
max_time=None,
convergence=3):
"""Parallel tempering workflow generator.
Args:
num_sweeps (int, optional):
Number of sweeps in the fixed temperature sampling.
num_replicas (int, optional):
Number of replicas (parallel states / workflow branches).
max_iter (int/None, optional):
Maximum number of iterations of the update/swaps loop.
max_time (int/None, optional):
Maximum wall clock runtime (in seconds) allowed in the update/swaps
loop.
convergence (int/None, optional):
Number of times best energy of the coldest replica has to repeat
before we terminate.
Returns:
Workflow (:class:`~hybrid.core.Runnable` instance).
"""
# expand single input state into `num_replicas` replica states
preprocess = SpawnParallelTemperingReplicas(num_replicas=num_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 `num_replicas-1` fixed-temperature-samplers
update = hybrid.Branches(
qpu, *[
FixedTemperatureSampler(num_sweeps=num_sweeps)
for _ in range(num_replicas - 1)
])
# replica exchange step: do the top-down sweep over adjacent pairs
# (good hot samples sink to bottom)
swap = SwapReplicasDownsweep()
# loop termination key function
def key(states):
if states is not None:
return states[-1].samples.first.energy
# replicas update/swap until Loop termination criteria reached
loop = hybrid.Loop(update | swap,
max_iter=max_iter,
max_time=max_time,
convergence=convergence,
key=key)
# collapse all replicas (although the bottom one should be the best)
postprocess = hybrid.MergeSamples(aggregate=True)
workflow = preprocess | loop | postprocess
return workflow