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def test_sa_sweeps(self):
iteration = 10
sampler = oj.SASampler()
res = sampler.sample_ising(self.h, self.J, num_reads=iteration)
self.assertEqual(iteration, len(res.energies))
sampler = oj.SASampler(num_reads=iteration)
res = sampler.sample_ising(self.h, self.J)
self.assertEqual(iteration, len(res.energies))
def test_sa(self):
initial_state = [1 for _ in range(self.size)]
response = oj.SASampler().sample_ising(
self.h, self.J, initial_state=initial_state, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [-1, -1, -1])
response = oj.SASampler(beta_max=100).sample_qubo(self.Q, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [0, 0, 0])
vaild_sche = [(beta, 1) for beta in np.linspace(-1, 1, 5)]
with self.assertRaises(ValueError):
sampler = oj.SASampler(schedule=vaild_sche)
sampler.sample_ising({}, {})
def test_sa_sweeps(self):
iteration = 10
sampler = oj.SASampler()
res = sampler.sample_ising(self.h, self.J, num_reads=iteration)
self.assertEqual(iteration, len(res.energies))
sampler = oj.SASampler(num_reads=iteration)
res = sampler.sample_ising(self.h, self.J)
self.assertEqual(iteration, len(res.energies))
def test_sa(self):
initial_state = [1 for _ in range(self.size)]
response = oj.SASampler().sample_ising(
self.h, self.J, initial_state=initial_state, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [-1, -1, -1])
response = oj.SASampler(beta_max=100).sample_qubo(self.Q, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [0, 0, 0])
vaild_sche = [(beta, 1) for beta in np.linspace(-1, 1, 5)]
with self.assertRaises(ValueError):
sampler = oj.SASampler(schedule=vaild_sche)
sampler.sample_ising({}, {})
def test_reverse_annealing(self):
seed_for_mc = 1
initial_state = [0, 0, 0]
qubo = {
(0, 0): 1, (1, 1): -1, (2, 2): 2,
(0, 1): 1, (1, 2): -1, (2, 0): -1
}
# solution is [0, 1, 0]
solution = [0, 1, 0]
# Reverse simulated annealing
# beta, step_length
reverse_schedule = [
[10, 3], [1, 3], [0.5, 3], [1, 3], [10, 5]
]
rsa_sampler = oj.SASampler(schedule=reverse_schedule, iteration=10)
res = rsa_sampler.sample_qubo(
qubo, initial_state=initial_state, seed=seed_for_mc)
self.assertListEqual(
solution,
list(res.min_samples['states'][0])
)
# Reverse simulated quantum annealing
# annealing parameter s, step_length
reverse_schedule = [
[1, 1], [0.3, 3], [0.1, 5], [0.3, 3], [1, 3]
]
rqa_sampler = oj.SQASampler(schedule=reverse_schedule, iteration=10)
res = rqa_sampler.sample_qubo(
qubo, initial_state=initial_state, seed=seed_for_mc)
self.assertListEqual(
def test_swendsenwang(self):
sampler = oj.SASampler()
initial_state = [1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1]
h = {0: -1, 10: -1}
J = {(i, i+1): -1 for i in range(10)}
res = sampler.sample_ising(h, J,
updater="swendsenwang",
seed=1, initial_state=initial_state)
self.assertListEqual(res.states[0], [1]*11)
def test_sa(self):
initial_state = [1 for _ in range(self.size)]
response = oj.SASampler().sample_ising(
self.h, self.J, initial_state=initial_state, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [-1, -1, -1])
response = oj.SASampler(beta_max=100).sample_qubo(self.Q, seed=1)
self.assertEqual(len(response.states), 1)
self.assertListEqual(response.states[0], [0, 0, 0])
vaild_sche = [(beta, 1) for beta in np.linspace(-1, 1, 5)]
with self.assertRaises(ValueError):
sampler = oj.SASampler(schedule=vaild_sche)
sampler.sample_ising({}, {})
import numpy as np
import openjij as oj
if __name__ == '__main__':
h = {0: -1}
J = {(0, 1): -1, (1, 2): -1}
# Simulated annealing (classical) 10 times
response = oj.SASampler(iteration=10).sample_ising(h, J)
# show the lowest energy solution in ten times
min_index = np.argmin(response.energies)
print("SA results: ", response.states[min_index])
# > SA results: [1, 1, 1]
# Simulated quantum annealing (quantum simulation) 10 times
response = oj.SQASampler(iteration=10).sample_ising(h, J)
# show the lowest energy solution in ten times
min_index = np.argmin(response.energies)
print("SQA results: ", response.states[min_index])
# > SQA results: [1, 1, 1]
import matplotlib.pyplot as plt
import openjij as oj
if __name__ == "__main__":
# make target instance
N = 10
h = {0: 1, 1: 1}
J = {}
for i in range(N-1):
for j in range(i+1, N):
J[(i, j)] = -1.0
true_ground_state = [-1]*N
sa_samp = oj.SASampler()
ground_energy = oj.BinaryQuadraticModel(h, J).calc_energy(true_ground_state)
# make benchmark target solver
def solver(time_param, iteration):
sa_samp.step_num = time_param
sa_samp.iteration = iteration
return sa_samp.sample_ising(h, J)
# benchmarking
b_res = oj.benchmark([true_ground_state], ground_energy, solver, time_param_list=np.arange(1, 161, 50))
plt.xlabel('annealing time')
plt.ylabel('error probability')
plt.plot(b_res['time'], b_res['error'])
plt.show()