def test_max_cut(self):
sampler = EmbeddingComposite(MockDWaveSampler())
m = 2
n = 2
t = 2
hoff = 2 * t
voff = n * hoff
mi = m * voff
ni = n * hoff
edges = []
# tile edges
edges.extend((k0, k1) for i in range(0, ni, hoff)
for j in range(i, mi, voff) for k0 in range(j, j + t)
for k1 in range(j + t, j + 2 * t))
# horizontal edges
edges.extend((k, k + hoff) for i in range(t, 2 * t)
for j in range(i, ni - hoff, hoff)
for k in range(j, mi, voff))
# vertical edges
edges.extend((k, k + voff) for i in range(t)
for j in range(i, ni, hoff)
for k in range(j, mi - voff, voff))
J = {edge: 1 for edge in edges}
h = {v: 0 for v in set().union(*J)}
response = sampler.sample_ising(h, J)
def start_annealing(layers: list, input_data, output_data, lagrange_propagation=1, qpu=False, stitch_kwargs=None):
if qpu:
sampler = EmbeddingComposite(
DWaveSampler(solver={'topology__type': 'pegasus', 'qpu': True}))
else:
sampler = tabu.TabuSampler()
bqm = get_qnn_bqm(layers, input_data, output_data, lagrange_propagation, stitch_kwargs)
# Check elements in the BQM
# for q in bqm.linear:
# if bqm.linear[q] != -1:
# print(q)
# for q in bqm.quadratic:
# if bqm.quadratic[q] != 2:
# print(q, bqm.quadratic[q])
# Run BQM and get the solution and the energy
sampleset = sampler.sample(bqm, num_reads=1000)
solution1 = sampleset.first.sample
energy1 = sampleset.first.energy
print("Nodes chosen ('layer_n.o'): ")
pprint(solution1)
print("Solution energy: ", energy1)
def run_dwave(self, H, sym):
"""Run a quantum annealing protocol using D-Wave's devices.
Args:
H (symbol): Hamiltonian that encodes the solution of a problem.
sys (tuple): symbols that make up the Hamiltonian.
Returns:
response: object that contains the details of the experiment.
"""
chainstrength = self.chainstrength
numruns, T = self.numruns, self.T
symbol_num = self.get_symbol_num(sym)
Q, constant = self.symbolic_to_dwave(H, symbol_num)
model = dimod.BinaryQuadraticModel.from_qubo(Q, offset = constant)
if not chainstrength:
chainstrength = 0
for i in Q.values():
chainstrength += abs(i)
chainstrength *= 3/len(Q)
#print(f'Automatic chain strength: {chainstrength}\n')
#else:
#print(f'Chosen chain strength: {chainstrength}\n')
sampler = EmbeddingComposite(DWaveSampler())
response = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='histogram')
return response
def __init__(self,
num_reads=100,
qpu_sampler=None,
anneal_schedule=None,
**runopts):
super(ReverseAnnealingAutoEmbeddingSampler, self).__init__(**runopts)
self.num_reads = num_reads
if anneal_schedule is None:
anneal_schedule = [[0, 1], [0.5, 0.5], [1, 1]]
self.anneal_schedule = anneal_schedule
if qpu_sampler is None:
qpu_sampler = DWaveSampler(solver={
'max_anneal_schedule_points__gte':
len(self.anneal_schedule)
})
# validate schedule, raising `ValueError` on invalid schedule or
# `RuntimeError` if anneal schedule not supported by QPU (this could
# happen only if user provided the `qpu_sampler`)
qpu_sampler.validate_anneal_schedule(anneal_schedule)
# convert the structured sampler to unstructured
if isinstance(qpu_sampler, dimod.Structured):
self.sampler = EmbeddingComposite(qpu_sampler)
else:
self.sampler = qpu_sampler
def test_return_embedding(self):
nodelist = [0, 1, 2]
edgelist = [(0, 1), (1, 2), (0, 2)]
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.NullSampler(), nodelist, edgelist))
sampleset = sampler.sample_ising({'a': -1}, {'ac': 1},
return_embedding=True)
self.assertIn('embedding', sampleset.info['embedding_context'])
embedding = sampleset.info['embedding_context']['embedding']
self.assertEqual(set(embedding), {'a', 'c'})
self.assertIn('chain_break_method',
sampleset.info['embedding_context'])
self.assertEqual(
sampleset.info['embedding_context']['chain_break_method'],
'majority_vote') # the default
self.assertIn('embedding_parameters',
sampleset.info['embedding_context'])
self.assertEqual(
sampleset.info['embedding_context']['embedding_parameters'],
{}) # the default
self.assertIn('chain_strength', sampleset.info['embedding_context'])
self.assertEqual(sampleset.info['embedding_context']['chain_strength'],
1.0) # the default
# default False
sampleset = sampler.sample_ising({'a': -1}, {'ac': 1})
self.assertNotIn('embedding_context', sampleset.info)
def test_return_embedding_as_class_variable(self):
nodelist = [0, 1, 2]
edgelist = [(0, 1), (1, 2), (0, 2)]
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.NullSampler(), nodelist, edgelist))
# temporarily change return_embedding_default
EmbeddingComposite.return_embedding_default = True
sampleset = sampler.sample_ising({'a': -1}, {'ac': 1})
self.assertIn('embedding', sampleset.info['embedding_context'])
embedding = sampleset.info['embedding_context']['embedding']
self.assertEqual(set(embedding), {'a', 'c'})
self.assertIn('chain_break_method',
sampleset.info['embedding_context'])
self.assertEqual(
sampleset.info['embedding_context']['chain_break_method'],
'majority_vote') # the default
self.assertIn('embedding_parameters',
sampleset.info['embedding_context'])
self.assertEqual(
sampleset.info['embedding_context']['embedding_parameters'],
{}) # the default
self.assertIn('chain_strength', sampleset.info['embedding_context'])
self.assertEqual(sampleset.info['embedding_context']['chain_strength'],
1.414) # the default
# restore the default
EmbeddingComposite.return_embedding_default = False
def test_singleton_variables(self):
sampler = EmbeddingComposite(MockDWaveSampler())
h = {0: -1., 4: 2}
J = {}
response = sampler.sample_ising(h, J)
# nothing failed and we got at least one response back
self.assertGreaterEqual(len(response), 1)
def run_data(self, chain_strength=5, num_reads=100):
if self.input_data == None:
raise Exception("You must include a dataset, use set_data")
print(f"Using Chain Strength: {chain_strength}")
print(f"Number of reads: {num_reads}")
self.result = EmbeddingComposite(self.sampler).sample_qubo(
self.input_data,
chain_strength=chain_strength,
num_reads=num_reads)
def sample_on_dwave(Q, Quantum=False):
bqm = dimod.BinaryQuadraticModel.from_numpy_matrix(Q)
if Quantum:
# Real
sampler = EmbeddingComposite(DWaveSampler(solver={'qpu': True}))
return sampler.sample(bqm, chain_strength=chstr)
# Simulated
sampler = neal.SimulatedAnnealingSampler()
return sampler.sample(bqm=bqm, num_reads=numr)
def test_embedding_parameters_sample(self):
child = dimod.StructureComposite(dimod.NullSampler(), [0, 1], [(0, 1)])
def my_find_embedding(S, T, a):
assert a == -1
return {v: [v] for v in set().union(*S)}
sampler = EmbeddingComposite(child, find_embedding=my_find_embedding)
# nothing breaks
sampler.sample_ising({0: -1}, {}, embedding_parameters={'a': -1})
def test_find_embedding_kwarg(self):
child = dimod.StructureComposite(dimod.NullSampler(), [0, 1], [(0, 1)])
def my_find_embedding(S, T):
# does nothing
return {v: [v] for v in set().union(*S)}
sampler = EmbeddingComposite(child, find_embedding=my_find_embedding)
# nothing breaks
sampler.sample_ising({0: -1}, {})
def test_warnings(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
# this will need chains lengths > 7
J = {uv: -1 for uv in itertools.combinations(range(40), 2)}
ss = sampler.sample_ising({}, J, warnings='SAVE')
self.assertIn('warnings', ss.info)
def test_chain_break_method_customization(self):
sampler = EmbeddingComposite(MockDWaveSampler())
with mock.patch('dwave.system.composites.embedding.unembed_sampleset'
) as mock_unembed:
sampler.sample_ising({'a': 1}, {},
chain_break_method=chain_breaks.discard)
# assert chain_break_method propagated to unembed_sampleset
__, kwargs = mock_unembed.call_args
self.assertEqual(kwargs['chain_break_method'],
chain_breaks.discard)
def _create_composite(self, embedding_parameters=None):
if (self._endpoint is not None) and (self._token is not None):
sampler = DWaveSampler(endpoint=self._endpoint, token=self._token, solver=self._solver)
else:
sampler = DWaveSampler(solver=self._solver)
if (embedding_parameters is not None) and isinstance(embedding_parameters, dict):
solver = EmbeddingComposite(sampler, embedding_parameters=embedding_parameters)
else:
solver = EmbeddingComposite(sampler)
return solver
def quantumOptim(h,J):
h,J=convertCompatible(h,J)
sampler = EmbeddingComposite(DWaveSampler(endpoint='https://cloud.dwavesys.com/sapi', token='[EnterTokenHere]', solver='DW_2000Q_2_1'))
response = sampler.sample_ising(h,J,num_reads=1000)
Q_energy=[]
Q_sigma=[]
for datum in response.data(['sample', 'energy']):
Q_energy.append(datum.energy)
Q_sigma.append(list(datum.sample.values()))
return Q_sigma[0],Q_energy[0]
def __init__(self, num_reads=100, qpu_sampler=None):
super(QPUSubproblemAutoEmbeddingSampler, self).__init__()
self.num_reads = num_reads
if qpu_sampler is None:
qpu_sampler = DWaveSampler()
# convert the structured sampler to unstructured
if isinstance(qpu_sampler, dimod.Structured):
self.sampler = EmbeddingComposite(qpu_sampler)
else:
self.sampler = qpu_sampler
def main(nqubits, instance, T, chainstrength, numruns, greedy, inspect):
"""
Args:
nqubits (int): number of qubits for the file that contains the
information of an Exact Cover instance.
instance (int): intance used for the desired number of qubits.
T (float):
Returns:
"""
control, solution, clauses = functions.read_file(nqubits, instance)
nqubits = int(control[0])
times = functions.times(nqubits, clauses)
sh, smap = functions.h_problem(nqubits, clauses)
Q, constant = functions.symbolic_to_dwave(sh, smap)
model = dimod.BinaryQuadraticModel.from_qubo(Q, offset = 0.0)
if not chainstrength:
chainstrength = dwave.embedding.chain_strength.uniform_torque_compensation(model)
print(f'Automatic chain strength: {chainstrength}\n')
else:
print(f'Chosen chain strength: {chainstrength}\n')
if solution:
print(f'Target solution that solves the problem: {" ".join(solution)}\n')
sampler = EmbeddingComposite(DWaveSampler())
if greedy:
solver_greedy = SteepestDescentSolver()
sampleset = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='raw')
response = solver_greedy.sample(model, initial_states=sampleset)
else:
response = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='histogram')
best_sample = response.record.sample[0]
best_energy = response.record.energy[0]
print(f'Best result found: {best_sample}\n')
print(f'With energy: {best_energy+constant}\n')
good_samples = response.record.sample[:min(len(response.record.sample), nqubits)]
good_energies = response.record.energy[:min(len(response.record.energy), nqubits)]
print(f'The best {len(good_samples)} samples found in the evolution are:\n')
for i in range(len(good_samples)):
print(f'Sample: {good_samples[i]} with energy: {good_energies[i]+constant}\n')
if inspect:
dwave.inspector.show(response)
def num_of_errors_in_chain_strengths(qpu=False):
jobs = {
"1": [(0, 2), (1, 1), (2, 1)],
"2": [(1, 1), (0, 1), (3, 2)],
"3": [(2, 1), (3, 1), (1, 1)]
}
strengths = (0.5, 1, 1.5, 1.8, 2.0, 2.1, 2.3, 2.5, 3.0, 3.5, 4.0)
errors = defaultdict(list)
for strength in strengths:
for i in range(12):
print("tick " + str(strength) + " " + str(i))
try:
bqm = get_jss_bqm(jobs,
8,
stitch_kwargs={'min_classical_gap': 2.0})
if qpu:
sampler = EmbeddingComposite(
DWaveSampler(solver={'qpu': True}))
sampleset = sampler.sample(bqm,
chain_strength=strength,
num_reads=1000)
else:
sampler = neal.SimulatedAnnealingSampler()
sampleset = sampler.sample(bqm, num_reads=1000)
sol_dict = printResults(sampleset, jobs)
errors[strength].append(sol_dict['error'])
except Exception as e:
print(f"error: {strength}")
print(e)
continue
medians = []
margins = []
for key, values in errors.items():
values.sort()
values = values[1:-1]
medians.append(median(values))
margins.append([
abs(values[0] - median(values)),
abs(values[-1] - median(values))
])
plt.errorbar(errors.keys(),
medians,
yerr=np.array(margins).T,
fmt='o-',
color='blue')
plt.xlabel('chain strength')
plt.ylabel('number of error solutions provided (out of 1000)')
# plt.show()
plt.savefig('chain_strength.png')
print(errors)
def num_of_errors_in_min_gap(qpu=False, start=1.0):
jobs = {"1": [(0, 2), (1, 1), (2, 1)],
"2": [(1, 1), (2, 2), (0, 1)],
"3": [(2, 2), (0, 1), (1, 2)]}
# best_solution = { "1": [0,2,4],
# "2": [0,2,4],
# "3": [0,2,3]}
# result: 5
import csv
# wyniki.csv structure:
# min_classical_gap, not found, incorrect, num_of_reads, 5, 6, 7, 8, 9, more
with open("wyniki_min_gap.csv", mode='a') as csvfile:
filewriter = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
# strengths = (25, 30, 35, 40, 45)
# strengths = list(range(20, 25))
from numpy import arange
gaps = list(arange(start, start+.5, 0.1))
num_reads = 1000
for gap in gaps:
for _ in range(10):
try:
bqm = get_jss_bqm(jobs, 8, stitch_kwargs={
'min_classical_gap': gap})
if qpu:
sampler = EmbeddingComposite(
DWaveSampler(solver={'qpu': True}))
sampleset = sampler.sample(
bqm, chain_strength=10.0, num_reads=num_reads)
else:
sampler = neal.SimulatedAnnealingSampler()
sampleset = sampler.sample(bqm, num_reads=num_reads)
sol_dict = printResults(sampleset, jobs)
except Exception as e:
print(f"error: {gap}")
print(e)
from time import sleep
sleep(60)
continue
result_row = [gap, sol_dict['error'], sol_dict['incorrect'],
num_reads] + [sol_dict[i] for i in range(5, 10)]
filewriter.writerow(result_row)
print('zapisane', gap)
from time import sleep
sleep(30)
def num_of_errors_in_times(qpu=False):
jobs = {
"1": [(0, 2), (1, 1), (0, 1)],
"2": [(1, 1), (0, 1), (2, 2)],
"3": [(2, 1), (2, 1), (1, 1)]
}
times = range(4, 12)
errors = defaultdict(list)
for time in times:
for i in range(12):
try:
bqm = get_jss_bqm(jobs,
time,
stitch_kwargs={'min_classical_gap': 2.0})
if qpu:
sampler = EmbeddingComposite(
DWaveSampler(solver={'qpu': True}))
sampleset = sampler.sample(bqm,
chain_strength=2,
num_reads=1000)
else:
sampler = neal.SimulatedAnnealingSampler()
sampleset = sampler.sample(bqm, num_reads=1000)
sol_dict = printResults(sampleset, jobs)
errors[time].append(sol_dict['error'])
except:
print(f"error: {time}")
continue
medians = []
margins = []
for key, values in errors.items():
values.sort()
values = values[1:-1]
medians.append(median(values))
margins.append([
abs(values[0] - median(values)),
abs(values[-1] - median(values))
])
plt.errorbar(errors.keys(),
medians,
yerr=np.array(margins).T,
fmt='o',
color='blue')
plt.xlabel('max_time value')
plt.ylabel('number of error solutions provided (out of 1000)')
# plt.show()
plt.savefig('times.png')
print(errors)
def test_chain_break_method_customization(self):
sampler = EmbeddingComposite(MockDWaveSampler())
def mock_unembed(*args, **kwargs):
self.assertIn('chain_break_method', kwargs)
self.assertEqual(kwargs['chain_break_method'], chain_breaks.discard)
mock_unembed.call_count += 1
return dwave.embedding.unembed_sampleset(*args, **kwargs)
mock_unembed.call_count = 0
with mock.patch('dwave.system.composites.embedding.unembed_sampleset', mock_unembed):
sampler.sample_ising({'a': 1}, {}, chain_break_method=chain_breaks.discard).resolve()
self.assertEqual(mock_unembed.call_count, 1)
def TSP(data, G, weights, s3_folder, machine):
lagrange_list = get_lagrange_list(G)
# run TSP with imported TSP routine
sampler = BraketDWaveSampler(s3_folder, machine)
sampler = EmbeddingComposite(sampler)
# set parameters
num_shots = 1000
start_city = 0
best_distance = sum(weights.values())
best_route = [None] * len(G)
# run HPO to find route
for lagrange in lagrange_list:
print('Running quantum annealing for TSP with Lagrange parameter=',
lagrange)
route = traveling_salesperson(G,
sampler,
lagrange=lagrange,
start=start_city,
num_reads=num_shots,
answer_mode="histogram")
# print distance
total_dist, distance_with_return = get_distance(route, data)
# update best values
if distance_with_return < best_distance:
best_distance = distance_with_return
best_route = route
return best_route, best_distance
class QPUSubproblemAutoEmbeddingSampler(Runnable, traits.SubproblemSampler):
"""A quantum sampler for a subproblem with automated heuristic minor-embedding.
Args:
num_reads (int, optional, default=100):
Number of states (output solutions) to read from the sampler.
qpu_sampler (:class:`dimod.Sampler`, optional, default=EmbeddingComposite(DWaveSampler())):
Quantum sampler such as a D-Wave system. If sampler is structured,
it will be converted to unstructured via :class:`~dwave.system.composited.EmbeddingComposite`.
Examples:
See examples on https://docs.ocean.dwavesys.com/projects/hybrid/en/latest/reference/samplers.html#examples.
"""
def __init__(self, num_reads=100, qpu_sampler=None):
super(QPUSubproblemAutoEmbeddingSampler, self).__init__()
self.num_reads = num_reads
if qpu_sampler is None:
qpu_sampler = DWaveSampler()
# convert the structured sampler to unstructured
if isinstance(qpu_sampler, dimod.Structured):
self.sampler = EmbeddingComposite(qpu_sampler)
else:
self.sampler = qpu_sampler
def __repr__(self):
return ("{self}(num_reads={self.num_reads!r}, "
"qpu_sampler={self.sampler!r})").format(self=self)
def next(self, state):
response = self.sampler.sample(state.subproblem,
num_reads=self.num_reads)
return state.updated(subsamples=response)
def QBoost(X_train, y_train, X_test, y_test):
NUM_READS = 1000
DW_PARAMS = {'num_reads': NUM_READS,
'auto_scale': True,
'num_spin_reversal_transforms': 10,
'postprocess': 'optimization',
}
from dwave.system.samplers import DWaveSampler
from dwave.system.composites import EmbeddingComposite
dwave_sampler = DWaveSampler(solver={'qpu': True})
emb_sampler = EmbeddingComposite(dwave_sampler)
from qboost import WeakClassifiers, QBoostClassifier
clf4 = QBoostClassifier(n_estimators=30, max_depth=2)
clf4.fit(X_train, y_train, emb_sampler, lmd=1.0, **DW_PARAMS)
y_train4 = clf4.predict(X_train)
y_test4 = clf4.predict(X_test)
from sklearn.metrics import accuracy_score
print('Accuracy for training data: \t', (accuracy_score(y_train, y_train4)))
print('Accuracy for test data: \t', (accuracy_score(y_test, y_test4)))
return clf4
def test_warning_chain_strength(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
J = {(0, 1): 100}
ss = sampler.sample_ising({}, J, warnings='SAVE')
self.assertIn('warnings', ss.info)
count = 0
for warning in ss.info['warnings']:
if issubclass(warning['type'], ChainStrengthWarning):
count += 1
self.assertEqual(count, 1)
def get_solver(solver_type: str, solver_name: str, token):
filters = {}
if solver_type.endswith("QPU") and solver_name in QPU_SOLVER_NAME_LIST:
filters[
"topology__type"] = "pegasus" if solver_name == "ADVANTAGE" else "chimera"
filters[
"name__contains"] = "Advantage_system" if solver_name == "ADVANTAGE" else "DW_2000Q"
if solver_type.endswith(
"HYBRID") and solver_name in HYBRID_SOLVER_NAME_LIST:
filters[
"name__contains"] = "version2" if solver_name == "HYBRID_V2" else "v1"
if solver_type == "SA":
solver = neal.SimulatedAnnealingSampler()
elif solver_type == "QPU":
solver = DWaveSampler(client="qpu", solver=filters, token=token)
solver = EmbeddingComposite(solver)
elif solver_type == "HYBRID":
solver = LeapHybridSampler(solver=filters, token=token)
elif solver_type == "FIXED_QPU":
solver = DWaveSampler(client="qpu", solver=filters, token=token)
solver = LazyFixedEmbeddingComposite(solver)
elif solver_type == "CLIQUE_FIXED_QPU":
solver = DWaveCliqueSampler(client="qpu", solver=filters, token=token)
else:
raise NotImplementedError(
"Solver {} is not implemented".format(solver_type))
return solver
def func_solveTSPdwave(
distanceMatrixLength, token, url, solver,
qubo): #Sends the QUBO to the annealer and retrieves the result
if distanceMatrixLength > 7: #The numReads variable specifies how many times the annealing will be performed on the QUBO, thus changing the amount of returned samples. The variable is scaled with the amount of countries to be visited, as more countries require more samples to get an accurate result.
numReads = 4000
elif distanceMatrixLength > 14:
numReads = 7000
else:
numReads = 2000
if '2000Q' in solver: #Sets the chain strength, which scales with the number of countries. In addition, the Advantage system provides better results with a lower chain strength than the 2000Q system.
chain = distanceMatrixLength * 100
else:
chain = int(round((distanceMatrixLength * 100) / 1.5, 0))
timeStart = time.time(
) #Makes a timestamp of when the annealing process began
try:
binaryResult = EmbeddingComposite(
DWaveSampler(
token=token, endpoint=url, solver=solver)).sample_qubo(
qubo, chain_strength=chain,
num_reads=numReads) #Sends the QUBO to the annealer
except Exception as error: #Attempts to catch an exception if one is returned by the D-Wave system
print(
f'An error has occured while trying to send the problem to the annealer: {error}'
)
print('The calculation has been cancelled, returning to main menu.')
return None, None, True
print(
f'\nBinarized optimal solution returned by the annealer:\n{binaryResult}'
) if debug == True else ''
return binaryResult, timeStart, None
def __init__(self, num_reads=100, qpu_sampler=None):
super(QPUSubproblemAutoEmbeddingSampler, self).__init__()
self.num_reads = num_reads
if qpu_sampler is None:
qpu_sampler = EmbeddingComposite(DWaveSampler())
self.sampler = qpu_sampler
def test_warnings_as_class_variable(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
# this will need chains lengths > 7
J = {uv: -1 for uv in itertools.combinations(range(40), 2)}
EmbeddingComposite.warnings_default = 'SAVE'
ss = sampler.sample_ising({}, J)
self.assertIn('warnings', ss.info)
EmbeddingComposite.warnings_default = 'IGNORE' # restore default
def run(problems, solver_factories):
results = OrderedDict()
# reuse the cloud client
qpu = EmbeddingComposite(DWaveSampler())
for problem in problems:
results[problem] = OrderedDict()
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
for name, factory in solver_factories:
case = '{!r} with {!r}'.format(problem, name)
try:
solver = factory(qpu=qpu)
init_state = State.from_sample(min_sample(bqm), bqm)
with tictoc(case) as timer:
solution = solver.run(init_state).result()
except Exception as exc:
print("FAILED {case}: {exc!r}".format(**locals()))
results[problem][name] = repr(exc)
else:
print("case={case!r}"
" energy={solution.samples.first.energy!r},"
" wallclock={timer.dt!r}".format(**locals()))
results[problem][name] = dict(
energy=solution.samples.first.energy, wallclock=timer.dt)