def ParallelTempering(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)
# fixed temperature sampling on all replicas in parallel
update = hybrid.Map(FixedTemperatureSampler(num_sweeps=num_sweeps))
# 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
subproblem_size = 100
print("BQM size: {}, subproblem size: {}".format(len(bqm), subproblem_size))
# Classical solvers
#subproblem = hybrid.EnergyImpactDecomposer(size=50, rolling_history=0.15)
#subproblem = hybrid.EnergyImpactDecomposer(size=1024, rolling_history=0.15, traversal="bfs")
# Parallel subproblem
subproblem = hybrid.Unwind(
#hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.15, traversal="bfs")
hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.1))
# QPU
#subsampler = hybrid.QPUSubproblemAutoEmbeddingSampler() | hybrid.SplatComposer()
subsampler = hybrid.Map(
hybrid.QPUSubproblemAutoEmbeddingSampler()) | hybrid.Reduce(
hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()
# Define the workflow
# iteration = hybrid.RacingBranches(
# hybrid.InterruptableTabuSampler(),
# hybrid.EnergyImpactDecomposer(size=2)
# | hybrid.QPUSubproblemAutoEmbeddingSampler()
# | hybrid.SplatComposer()
# ) | hybrid.ArgMin()
#iteration = hybrid.RacingBranches(
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
#hybrid.SimulatedAnnealingProblemSampler(),
subproblem | subsampler) | hybrid.ArgMin()
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))
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# 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()
