def suzuki_sampling(input_df, start_row, end_row, output_folder_path):
df = input_df.copy()
NUM_SAMPLES = 100
for row_id in range(start_row-1, end_row):
row = df.loc[row_id]
n = int(row['n'])
v = row['v_1':'v_%d' % n]
k = int(row['k'])
r = int(row['r'])
scale = float(row['scale'])
cols = ['n'] + ['v_%d' % i for i in range(1, n+1)] + ['k', 'r', 'scale', 'sampled error', 'suzuki error']
row_df = pd.DataFrame(columns=cols)
chain = hchain.HeisenbergChain(n, v)
sample_length = (5**(k-1))
suzuki_vector = approx.suzuki(k)
for _ in range(NUM_SAMPLES):
noise = np.random.normal(loc=0, scale=scale/sample_length, size=sample_length)
sample_vector = np.array(suzuki_vector) + noise
sampled_err = approx.error(chain, approx.r_copies(sample_vector, r), t=2*n)
suz_err = approx.error(chain, approx.suzuki_solution(k, r), t=2*n)
row_df.loc[len(row_df)+1] = [n] + list(v) + [k, r, scale, sampled_err, suz_err]
row_df.to_csv(os.path.join(output_folder_path, 'row_%d.csv'%(row_id+1)))
def testSuzuki(self):
p_2 = 1 / (4 - 4**(1 / 3))
x = [p_2, p_2, 1 - 4 * p_2, p_2, p_2]
self.assertTrue(np.allclose(x, approx.suzuki(2)))
p_3 = 1 / (4 - 4**(1 / 5))
y = [
p_3 * p_2, p_3 * p_2, p_3 * (1 - 4 * p_2), p_3 * p_2, p_3 * p_2,
p_3 * p_2, p_3 * p_2, p_3 * (1 - 4 * p_2), p_3 * p_2, p_3 * p_2,
(1 - 4 * p_3) * p_2, (1 - 4 * p_3) * p_2,
(1 - 4 * p_3) * (1 - 4 * p_2), (1 - 4 * p_3) * p_2,
(1 - 4 * p_3) * p_2, p_3 * p_2, p_3 * p_2, p_3 * (1 - 4 * p_2),
p_3 * p_2, p_3 * p_2, p_3 * p_2, p_3 * p_2, p_3 * (1 - 4 * p_2),
p_3 * p_2, p_3 * p_2
]
self.assertTrue(np.allclose(y, approx.suzuki(3)))
def greedy():
""" Run greedy hillclimber. Enumerate neighbourhood and choose best. """
chain = hchain.HeisenbergChain(num_qubits, v)
steps = [-step_size, 0, step_size]
neighbourhood_size = len(steps)**num_p_values
suzuki = np.array(approx.suzuki(k))
suzuki_error = approx.error(chain, approx.r_copies(suzuki, r), t)
print('Suzuki error: {}'.format(suzuki_error))
# Start at Suzuki
current = suzuki
current_error = suzuki_error
# Start at 1/p_length
# current = np.array([0.2] * 5)
# current_error = approx.error(chain, approx.r_copies(current, r), t)
# Start at suzuki permutation
# import random
# random.seed(1234)
# suzuki_coeff = approx.suzuki(k)
# random.shuffle(suzuki_coeff)
# current = np.array(suzuki_coeff)
# current_error = approx.error(chain, approx.r_copies(current, r), t)
for gen in range(1, gens+1):
current_error_percent = 100 * (current_error / suzuki_error)
print('Gen: {} Best: {} Percent: {} '.format(gen, current_error, current_error_percent))
all_neighbours = list(range(1, neighbourhood_size))
random.shuffle(all_neighbours
)
for neighbour_index in all_neighbours:
move = [step_size * (coeff - 1) for coeff in ternary(neighbour_index, num_p_values)]
neighbour = current + move
neighbour_err = approx.error(chain, approx.r_copies(neighbour, r), t)
neighbour_err_percent = 100 * (neighbour_err / suzuki_error)
print('Step {} Neighbour {}/{} error: {}, error %: {}'.format(gen, neighbour_index, neighbourhood_size,
neighbour_err, neighbour_err_percent))
if neighbour_err < current_error:
current = neighbour
current_error = neighbour_err
current_error_percent = 100 * (current_error / suzuki_error)
print('New best: error: {} percent: {} solution: {})'.format(current_error, current_error_percent,
current))
break
def run_cmaes_p(v, k, r, seed, generations, sig=None):
suzuki = approx.suzuki(k)
if sig == None:
sig = 1e-5 / len(suzuki)
chain = hchain.HeisenbergChain(len(v), v)
random.seed(seed)
np.random.seed(seed)
# Error from target
def target_error(ind):
if NORMALISE:
norm_ind = norm_f(ind)
else:
norm_ind = ind
final_ind = approx.r_copies(ind, r)
return approx.error(chain, final_ind, t=2 * chain.n),
toolbox = base.Toolbox()
toolbox.register("evaluate", target_error)
strategy = cma.Strategy(centroid=suzuki, sigma=sig)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
pop, log = algorithms.eaGenerateUpdate(toolbox,
ngen=generations,
stats=stats,
halloffame=hof,
verbose=True)
return pop, log, hof
def greedy():
""" Run greedy hillclimber. Enumerate neighbourhood and choose best. """
random.seed(1234)
chain = hchain.HeisenbergChain(num_qubits, v)
suzuki = np.array(approx.suzuki(k))
suzuki_error = approx.error(chain, approx.r_copies(suzuki, r), t)
print('Suzuki error: {}'.format(suzuki_error))
# Start at Suzuki
current = suzuki
current_error = suzuki_error
for gen in range(1, gens + 1):
current_error_percent = 100 * (current_error / suzuki_error)
print('Gen: {} Best: {} Percent: {} '.format(gen, current_error,
current_error_percent))
first = random.randint(1, num_p_values)
second = random.randint(1, num_p_values)
neighbour = current.copy()
neighbour[first - 1] = neighbour[first - 1] + step_size
neighbour[second - 1] = neighbour[second - 1] - step_size
neighbour_err = approx.error(chain, approx.r_copies(neighbour, r), t)
neighbour_err_percent = 100 * (neighbour_err / suzuki_error)
print('Step {} error: {}, error %: {}'.format(gen, neighbour_err,
neighbour_err_percent))
if neighbour_err < current_error:
current = neighbour
current_error = neighbour_err
current_error_percent = 100 * (current_error / suzuki_error)
print('New best: error: {} percent: {} solution: {})'.format(
current_error, current_error_percent, current))
def testExpandVals(self):
for k in range(1, 10):
suz1 = approx.suzuki(k)
suz2 = approx.expand_vals(approx.suzuki_vals(k))
self.assertTrue(np.allclose(suz1, suz2))