def SimplifiedQbsolv(max_iter=10,
max_time=None,
convergence=3,
energy_threshold=None,
max_subproblem_size=30):
"""Races a Tabu solver and a QPU-based sampler of flip-energy-impact induced
subproblems.
For arguments description see: :class:`~hybrid.reference.kerberos.Kerberos`.
"""
energy_reached = None
if energy_threshold is not None:
energy_reached = lambda en: en <= energy_threshold
workflow = hybrid.Loop(hybrid.Race(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(
size=max_subproblem_size, rolling=True, rolling_history=0.15)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin()
| hybrid.TrackMin(output=True),
max_iter=max_iter,
max_time=max_time,
convergence=convergence,
terminate=energy_reached)
return workflow
import hybrid
# load a problem
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)
import sys
import dimod
import hybrid
# load a problem
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# define the workflow
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=50, rolling=True, rolling_history=0.15)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()
) | hybrid.ArgMin() | hybrid.TrackMin(output=True)
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()
# show results
print("Solution: sample={.samples.first}".format(solution))
#subproblem = hybrid.EnergyImpactDecomposer(size=50, rolling_history=0.15)
subproblem = hybrid.Unwind(
hybrid.EnergyImpactDecomposer(size=100,
rolling_history=0.15,
traversal='bfs'))
# QPU
#subsampler = hybrid.QPUSubproblemAutoEmbeddingSampler() | hybrid.SplatComposer()
qpu_sampler = hybrid.Map(
hybrid.QPUSubproblemAutoEmbeddingSampler()) | hybrid.Reduce(
hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()
rand_sampler = hybrid.Map(hybrid.RandomSubproblemSampler()) | hybrid.Reduce(
hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()
iteration = hybrid.RacingBranches(
hybrid.InterruptableTabuSampler(),
subproblem | qpu_sampler) | hybrid.ArgMin() | hybrid.TrackMin(output=True)
workflow = hybrid.LoopUntilNoImprovement(iteration, convergence=5)
start_t = time.perf_counter()
# Convert to dimod sampler and run workflow
result = hybrid.HybridSampler(workflow).sample(bqm)
elapsed_t = time.perf_counter() - start_t
# show execution profile
#hybrid.profiling.print_counters(workflow)
print("Solution: sample={}".format(result.first)) # doctest: +SKIP
#dwave.inspector.show(result)
print("Elapsed time: {}".format(elapsed_t))
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) | hybrid.ArgMin()
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
subproblems | subsampler) | hybrid.ArgMin() | hybrid.TrackMin(output=True)
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()
# show execution profile
hybrid.profiling.print_counters(main)
# show results
print("Solution: sample={.samples.first}".format(solution))
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import dimod
import hybrid
# load a problem
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# define the workflow
iteration = hybrid.Race(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=50, rolling=True, rolling_history=0.15)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin() | hybrid.TrackMin(output=True)
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()
# show results
print("Solution: sample={.samples.first}".format(solution))