def anneal(C_i, C_ij, mu, sigma, l, strength_scale, energy_fraction, ngauges,
max_excited_states):
#Initialising h and J as dictionnaries
h = {}
J = {}
for i in range(len(C_i)):
h_i = -2 * sigma[i] * C_i[i]
for j in range(len(C_ij[0])):
if j > i:
J[(i, j)] = float(2 * C_ij[i][j] * sigma[i] * sigma[j])
h_i += 2 * (sigma[i] * C_ij[i][j] * mu[j])
h[i] = h_i
#applying cutoff
print("Number of J before : " + str(len(J))) #J before cutoff
float_vals = []
for i in J.values():
float_vals.append(i)
cutoff = np.percentile(float_vals, AUGMENT_CUTOFF_PERCENTILE)
to_delete = []
for k, v in J.items():
if v < cutoff:
to_delete.append(k)
for k in to_delete:
del J[k]
print("Number of J after : " + str(len(J))) # J after cutof
new_Q = {}
isingpartial = {}
if FIXING_VARIABLES:
#Optimising heuristically the number of coupling terms
Q, _ = dimod.ising_to_qubo(h, J, offset=0.0)
bqm = dimod.BinaryQuadraticModel.from_qubo(Q, offset=0.0)
simple = dimod.fix_variables(bqm, sampling_mode=False)
if simple == {}:
new_Q = Q
else:
Q_indices = []
for i in Q:
if i in simple.keys():
continue
else:
Q_indices.append(i)
new_Q = {key: Q[key] for key in Q_indices}
print('new length', len(new_Q))
isingpartial = simple
if (not FIXING_VARIABLES) or len(new_Q) > 0:
mapping = []
offset = 0
for i in range(len(C_i)):
if i in isingpartial:
mapping.append(None)
offset += 1
else:
mapping.append(i - offset)
if FIXING_VARIABLES:
new_Q_mapped = {}
for (first, second), val in new_Q.items():
new_Q_mapped[(mapping[first], mapping[second])] = val
h, J, _ = dimod.qubo_to_ising(new_Q_mapped)
#Run gauges
qaresults = []
print("Number of variables to anneal :" + str(len(h)))
for g in range(ngauges):
#Finding embedding
qaresult = []
embedded = False
for attempt in range(5):
a = np.sign(np.random.rand(len(h)) - 0.5)
float_h = []
for i in h.values():
float_h.append(i)
h_gauge = float_h * a
J_gauge = {}
for i in range(len(h)):
for j in range(len(h)):
if (i, j) in J:
J_gauge[(i, j)] = J[(i, j)] * a[i] * a[j]
try:
print("Trying to find embeding")
sampler = EmbeddingComposite(
DWaveSampler(token='secret_token'))
embedded = True
break
except ValueError: # no embedding found
print('no embedding found')
embedded = False
continue
if not embedded:
continue
print("emebeding found")
print("Quantum annealing")
try_again = True
while try_again:
try:
#Annealing, saving energy and sample list
sampleset = sampler.sample_ising(
h_gauge,
J_gauge,
chain_strength=strength_scale,
num_reads=200,
annealing_time=20)
try_again = False
except:
print('runtime or ioerror, trying again')
time.sleep(10)
try_again = True
print("Quantum done")
qaresult.append(sampleset.record[0][0].tolist())
qaresult = np.asarray(qaresult)
qaresult = qaresult * a
qaresults[g * nreads:(g + 1) * nreads] = qaresult
full_strings = np.zeros((len(qaresults), len(C_i)))
full_strings = np.asarray(full_strings)
qaresults = np.asarray(qaresults)
if FIXING_VARIABLES:
j = 0
for i in range(len(C_i)):
if i in isingpartial:
full_strings[:, i] = 2 * isingpartial[i] - 1
else:
full_strings[:, i] = qaresults[:, j]
j += 1
else:
full_strings = qaresults
s = np.asarray(full_strings)
energies = np.zeros(len(qaresults))
s[np.where(s > 1)] = 1.0
s[np.where(s < -1)] = -1.0
bits = len(s[0])
for i in range(bits):
energies += 2 * s[:, i] * (-sigma[i] * C_i[i])
for j in range(bits):
if j > i:
energies += 2 * s[:, i] * s[:, j] * sigma[i] * sigma[
j] * C_ij[i][j]
energies += 2 * s[:, i] * sigma[i] * C_ij[i][j] * mu[j]
unique_energies, unique_indices = np.unique(energies,
return_index=True)
ground_energy = np.amin(unique_energies)
if ground_energy < 0:
threshold_energy = (1 - energy_fraction) * ground_energy
else:
threshold_energy = (1 + energy_fraction) * ground_energy
lowest = np.where(unique_energies < threshold_energy)
unique_indices = unique_indices[lowest]
if len(unique_indices) > max_excited_states:
sorted_indices = np.argsort(
energies[unique_indices])[-max_excited_states:]
unique_indices = unique_indices[sorted_indices]
print("unique indices : ", unique_indices)
print(type(unique_indices[0]))
print(type(full_strings))
final_answers = full_strings[unique_indices]
print('number of selected excited states', len(final_answers))
return final_answers
else:
final_answer = []
print("Evrything resolved by FIXING_VARIABLES")
for i in range(len(C_i)):
if i in isingpartial:
final_answer.append(2 * isingpartial[i] - 1)
final_answer = np.array(final_answer)
return np.array([final_answer])
def test_typical(self):
h = {i: v for i, v in enumerate([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4])}
j = {
(0, 5): 2,
(0, 8): 4,
(1, 4): -5,
(1, 7): 1,
(2, 0): 5,
(2, 1): 4,
(3, 0): -1,
(3, 6): -3,
(3, 8): 3,
(4, 0): 2,
(4, 7): 3,
(4, 9): 3,
(5, 1): 3,
(6, 5): -4,
(6, 7): -4,
(7, 1): -4,
(7, 8): 3,
(8, 2): -4,
(8, 3): -3,
(8, 6): -5,
(8, 7): -4,
(9, 0): 4,
(9, 1): -1,
(9, 4): -5,
(9, 7): 3
}
q, offset = ising_to_qubo(h, j)
# norm_q = normalized_matrix(q)
ans = {
(0, 0): -42,
(0, 2): 20,
(0, 3): -4,
(0, 4): 8,
(0, 5): 8,
(0, 8): 16,
(0, 9): 16,
(1, 1): -4,
(1, 2): 16,
(1, 4): -20,
(1, 5): 12,
(1, 7): -12,
(1, 9): -4,
(2, 2): -16,
(2, 8): -16,
(3, 3): 4,
(3, 6): -12,
(4, 4): 2,
(4, 7): 12,
(4, 9): -8,
(5, 5): -2,
(5, 6): -16,
(6, 6): 34,
(6, 7): -16,
(6, 8): -20,
(7, 7): 8,
(7, 8): -4,
(7, 9): 12,
(8, 8): 18
}
for (u, v), bias in normalized_matrix(q).items():
self.assertIn((u, v), ans)
self.assertEqual(bias, ans[(u, v)])
self.assertEqual(offset, 2)
def test_no_zeros(self):
q, offset = ising_to_qubo({0: 0, 0: 0, 0: 0}, {(0, 0): 0, (4, 5): 0})
self.assertEqual(q, {(0, 0): 0.0})
self.assertEqual(offset, 0)
def test_j_diag(self):
q, offset = ising_to_qubo({}, {(0, 0): 1, (300, 300): 99})
self.assertEqual(q, {(0, 0): 0.0, (300, 300): 0.0})
self.assertEqual(offset, 100)
def test_trivial(self):
q, offset = ising_to_qubo({}, {})
self.assertEqual(q, {})
self.assertEqual(offset, 0)
