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import numpy as np
from tqdm import tqdm
from quantum_perceptron.utils import (
calculate_succ_probability
)
from quantum_perceptron.perceptron import Perceptron
def generate_training_samples(data: np.ndarray,
num_positive_samples: int,
num_negative_samples: int) -> np.ndarray:
"""
From the entire dataset, generate training samples.
"""
pos_inds = np.where(data[:, 1] == 1)[0]
neg_inds = np.where(data[:, 1] == 0)[0]
if len(pos_inds) < num_positive_samples:
num_positive_samples = len(pos_inds)
if len(neg_inds) < num_negative_samples:
num_negative_samples = len(neg_inds)
sampled_neg_inds = np.random.choice(neg_inds,
num_negative_samples,
replace=False)
sampled_pos_inds = np.random.choice(pos_inds,
num_positive_samples,
replace=False)
new_data = np.vstack((data[sampled_pos_inds], data[sampled_neg_inds]))
np.random.shuffle(new_data)
return new_data
def generate_dataset(num_qubits: int = 4,
fixed_weight: int = 626,
dir_path: str = './data/',
threshold: float = 0.5,
num_runs: int = 8192,
create_training_samples: bool = True,
num_pos_train_samples: int = 50,
num_neg_train_samples: int = 3000):
"""
Generate training dataset with fixed weight value.
Args:
num_qubits: `int` representing number of qubits.
fixed_weight: `int` representing the fixed weight value.
dir_path: `str` representing the directory path.
"""
num_samples = np.power(2, np.power(2, num_qubits))
data = np.empty([num_samples, 2], dtype=np.int64)
p = Perceptron(num_qubits, fixed_weight, 0)
for i in tqdm(range(num_samples)):
p.input = i
p.build_circuit()
prob = calculate_succ_probability(p.measure_circuit(num_runs))
if prob > threshold:
label = 1
else:
label = 0
data[i][0] = i
data[i][1] = label
print("Number of positive samples: {}".format(
np.sum(data[:, 1] == 1)
))
print("Number of negative samples: {}".format(
np.sum(data[:, 1] == 0)
))
filename = 'sample_space_qubits_{}_fweight_{}.txt'.format(
num_qubits, fixed_weight
)
np.savetxt(dir_path + filename, data, fmt='%i,%i', delimiter=',')
print('Saved data to {}'.format(dir_path + filename))
if create_training_samples:
train_data = generate_training_samples(
data, num_pos_train_samples, num_neg_train_samples
)
train_filename = 'train_space_qubits_{}_fweight_{}.txt'.format(
num_qubits, fixed_weight
)
np.savetxt(dir_path + train_filename,
train_data,
fmt='%i,%i',
delimiter=',')
print('Saved training data to {}'.format(dir_path + train_filename))
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