def hybrid_solver():
workflow = hybrid.LoopUntilNoImprovement(hybrid.RacingBranches(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=20)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin(),
convergence=3)
return hybrid.HybridSampler(workflow)
def hybrid_solver():
workflow = hybrid.Loop(hybrid.RacingBranches(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(
size=30, rolling=True, rolling_history=0.75)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin(),
convergence=1)
return hybrid.HybridSampler(workflow)
# 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.
from __future__ import print_function
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
workflow = hybrid.Loop(hybrid.RacingBranches(
hybrid.Identity(), hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=50, rolling=True, traversal='bfs')
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin(),
convergence=3)
# create a dimod sampler that runs the workflow and sample
result = hybrid.HybridSampler(workflow).sample(bqm)
# show results
print("Solution: sample={.first}".format(result))
