def qbsolv_solver(self, ising=True):
""" qbsolv is for breaking up large optmisation problems into smaller
ones for the D-Wave machines.
This function has not been properly implemented yet.
"""
if ising:
response = QBSolv().sample_ising(self.h, self.j)
else:
response = QBSolv().sample_qubo(self.q_dict)
min_energy = 0
for sample in response.data(['sample', 'energy']):
sample_dict = {
'sample': {k: int((1 + sample[0][k]) / 2)
for k in sample[0]}
}
sample_dict.update({'energy': sample[1]})
self.samples.append(sample_dict)
min_energy = min(sample[1], min_energy)
self.solution = {
f'x{k+1}': v
for k, v in sample_dict['sample'].items()
if k < len(self.masses)
} if min_energy == sample[1] else self.solution
self._solution_blocks()
return response
#from dwave_qbsolv import QBSolv
#from dwave.system.samplers import DWaveSampler
#from dwave.system.composites import EmbeddingComposite
#subqubo_size = 30
#sampler = EmbeddingComposite(DWaveSampler())
#resp = QBSolv().sample_qubo(Q, solver=sampler, solver_limit=subqubo_size)
# # Use LeapHybridSampler() for faster QPU access
# sampler = LeapHybridSampler()
# resp = sampler.sample_qubo(Q)
# First solution is the lowest energy solution found
sample = next(iter(resp))
# Display energy for best solution found
print('Energy: ', next(iter(resp.data())).energy)
# Print route for solution found
route = [-1] * node_count
for node in sample:
if sample[node] > 0:
j = node % node_count
v = (node - j) / node_count
route[j] = int(v)
# Compute and display total mileage
mileage = 0
for i in range(node_count):
mileage += distances[route[i]][route[(i + 1) % node_count]]
print('Mileage: ', mileage)
from dwave_qbsolv import QBSolv
import neal
import itertools
import random
qubo_size = 2000
subqubo_size = 30
Q = {
t: random.uniform(-1, 1)
for t in itertools.product(range(qubo_size), repeat=2)
}
sampler = neal.SimulatedAnnealingSampler()
response = QBSolv().sample_qubo(Q, solver=sampler, solver_limit=subqubo_size)
for sample in response.data():
print(sample[0], "Energy: ", sample[1], "Occurrences: ", sample[2])
else:
print('Sampling with classical solver...')
sampler = 'sim'
res = QBSolv().sample_qubo(Q, num_repeats=10)
# check constraints
samples = list(res.samples())
energy = list(res.data_vectors['energy'])
n_print = 20
energies = []
n_violations = []
n_occ = []
for num, sample in enumerate(
res.data(fields=['sample', 'energy', 'num_occurrences'],
sorted_by='energy')):
if num < n_print:
sample_arr = np.array(
[sample.sample[key] for key in sample.sample.keys()])
violations = check_constraints(
sample_arr,
save='result/instantinsanity/nonsoln_problem{0}{1}'.format(
num, sampler),
plot_soln=True)
energy = sample.energy
if num == 0:
n_occ.append(sample.num_occurrences)
elif energy == energies[-1]:
n_occ[-1] += sample.num_occurrences
else:
