def solve_multi_data(self, verbose=False, target=None, num_repeats=200):
if self.qubo is None:
self.pre_process()
if verbose:
print("Solving multiple solutions of MAX-SMTI with Qbsolv")
if self.qubo_size == 0 or self.qubo_size == 1:
return [Solution(self.matching, self.pre_evaluated_solution)]
if self.mode == "np": # more memory intensive
response = QBSolv().sample(BinaryQuadraticModel.from_numpy_matrix(self.qubo), num_repeats=num_repeats,
target=target, algorithm=SOLUTION_DIVERSITY)
elif self.mode == "bqm":
response = QBSolv().sample(self.qubo, num_repeats=num_repeats, target=target)
else:
raise Exception(f"mode: {self.mode} cannot be solved yet")
if verbose:
print(response)
for index, sample in enumerate(list(response.samples())):
match, valid = self.encode(sample)
print(index, ":", Solution(self.matching, match).is_stable(), match, valid)
samples = pd.DataFrame()
for sample, energy, occ in response.record:
match, valid = self.encode_qa(sample.tolist())
stable, size = Solution(self.matching, match).is_stable()
samples = samples.append({"match": match, "sample": sample.tolist(),
"energy": energy, "occ": occ,
"valid": valid, "stable": stable, "size": size}, ignore_index=True)
return samples
def solve_multi(self, verbose=False, num_repeats=200):
if self.qubo is None:
self.pre_process()
if verbose:
print("Solving multiple solutions of MAX-SMTI with Qbsolv")
if self.qubo_size == 0 or self.qubo_size == 1:
return [Solution(self.matching, self.pre_evaluated_solution)]
if self.mode == "np": # more memory intensive
response = QBSolv().sample(BinaryQuadraticModel.from_numpy_matrix(self.qubo), num_repeats=num_repeats)
elif self.mode == "bqm":
response = QBSolv().sample(self.qubo, num_repeats=num_repeats)
else:
raise Exception(f"mode: {self.mode} cannot be solved yet")
if verbose:
print(response)
for index, sample in enumerate(list(response.samples())):
match, valid = self.encode(sample)
print(index, ":", Solution(self.matching, match).is_stable(), match, valid)
opt_en = self.get_optimal_energy(self.matching.size)
solutions = []
for sample, energy, occ in response.record:
if energy == opt_en:
match, valid = self.encode_qa(sample.tolist())
if verbose and not valid:
print("Invalid encoding and valid energy!")
solutions.append(Solution(self.matching, match))
return solutions
def solve(self, verbose=False, num_repeats=50, target=None, debug=False):
if self.qubo is None:
self.pre_process()
if verbose:
print("Solving MAX-SMTI with Qbsolv")
if self.qubo_size == 0 or self.qubo_size == 1:
if debug:
return None
return Solution(self.matching, self.pre_evaluated_solution)
if self.mode == "np": # more memory intensive
response = QBSolv().sample(BinaryQuadraticModel.from_numpy_matrix(self.qubo), num_repeats=num_repeats,
target=target)
elif self.mode == "bqm":
response = QBSolv().sample(self.qubo, num_repeats=num_repeats, target=target)
else:
raise Exception(f"mode: {self.mode} cannot be solved yet")
if debug:
return response
if verbose:
print(response)
for index, sample in enumerate(list(response.samples())):
match, valid = self.encode(sample)
print(index, ":", Solution(self.matching, match).is_stable(), match, valid)
energies = list(response.data_vectors['energy'])
min_en = min(energies)
ret_match, valid = self.encode(list(response.samples())[energies.index(min_en)])
return Solution(self.matching, ret_match, energy=min_en)
def solve_multi(self, verbose=True, num_repeats=100, target=None):
if self.qubo is None:
self.create_qubo()
if self.mode == "np": # more memory intensive
response = QBSolv().sample(BinaryQuadraticModel.from_numpy_matrix(self.qubo), target=target)
elif self.mode == "bqm":
response = QBSolv().sample(self.qubo, num_repeats=num_repeats, target=target)
else:
raise Exception(f"mode: {self.mode} cannot be solved yet")
if verbose:
print(response)
n = self.matching.size
stable_energy = -3 / 2 * self.p1 * (n - 1) * n
energies = list(response.data_vectors['energy'])
samples = enumerate(list(response.samples()))
allowed_samples = [sample for idx, sample in samples if energies[idx] == stable_energy]
return [Solution(self.matching, self.encode(sample)) for sample in allowed_samples]
def solve(self, verbose=False, num_repeats=100, target=None):
if self.qubo is None:
self.create_qubo()
if self.mode == "np": # more memory intensive
response = QBSolv().sample(BinaryQuadraticModel.from_numpy_matrix(self.qubo), target=target)
elif self.mode == "bqm":
response = QBSolv().sample(self.qubo, num_repeats=num_repeats, target=target)
else:
raise Exception(f"mode: {self.mode} cannot be solved yet")
if verbose:
print(response)
energies = list(response.data_vectors['energy'])
min_en = min(energies)
ret_match = self.encode(list(response.samples())[energies.index(min_en)])
return Solution(self.matching, ret_match)
