def grad_frobenius(self, mini_batch_f, mini_batch_u):
# This class method calculates the gradient self.grad_U
for l in range(self.N):
self.grad_U[l] = np.zeros((self.N, self.N), dtype=complex)
# We occupy a gradient for the subsequent calculation of the sum
# Calculate self.list_A and self.list_B
for k in range(self.mini_batch_size):
fl = create_list_fl(mini_batch_f[k], self.N)
self.list_A[k][self.N - 1] = fl[self.N]
for l in range(self.N - 2, -1, -1):
self.list_A[k][l] = np.dot(self.list_A[k][l + 1], np.dot(self.list_U[l + 1], fl[l + 1]))
self.list_B[k][0] = fl[0]
for l in range(1, self.N, 1):
self.list_B[k][l] = np.dot(np.dot(fl[l], self.list_U[l - 1]), self.list_B[k][l - 1])
u_target = mini_batch_u[k]
u_result = interferometer(fl, self.list_U, self.N)
for l in range(self.N):
for p in range(self.N):
for t in range(self.N):
a = self.list_A[k][l]
b = self.list_B[k][l]
d = self.D[p][t]
grad_u_x = np.dot(a, np.dot(d, b))
grad_u_y = 1j * np.dot(a, np.dot(d, b))
self.grad_U[l][p][t] += (2 / self.N) * np.sum((u_result - u_target).conj() * grad_u_x).real + \
1j * (2 / self.N) * np.sum((u_result - u_target).conj() * grad_u_y).real
for l in range(self.N):
self.grad_U[l] = self.grad_U[l] / self.mini_batch_size # Average the gradient over the mini-packet
def func_sst(x, network, mini_batch_f, mini_batch_u, n):
c = 0.0 # The cost function itself, which must be calculated on the mini-package
mini_batch_size = len(mini_batch_f)
list_u = transform_to_matrix(
x, n) # Restored our list of trial basis matrices
for k in range(mini_batch_size):
fl = create_list_fl(mini_batch_f[k], n)
c = c + sst(interferometer(fl, list_u, n), mini_batch_u[k])
c = c / len(mini_batch_f)
return c # The function returns a real number
def save_sample_unitary_matrices(n, m, file_name1, file_name2):
# Generate random phases for the sample (M different matrices, size N by N)
fm = []
for k in range(m):
fm.append(2 * 3.141592 * np.random.rand(n, n))
# Create a list from U1, ..., UN
file1 = open(file_name1, 'r')
list_u = []
for l in range(n):
u = np.zeros((n, n), dtype=complex)
for i in range(n):
for j in range(n):
real = float(file1.readline())
imag = float(file1.readline())
u[i][j] = real + 1j * imag
s = file1.readline() # Read the empty line
list_u.append(u)
file1.close()
um = [] # List of resulting unitary matrices of size N by N
# We pass through the sample
for k in range(m):
um.append(interferometer(create_list_fl(fm[k], n), list_u, n))
file2 = open(file_name2, 'w')
for k in range(
m
): # We go through the selection and write phase matrices to a file
for i in range(n):
for j in range(n):
file2.write(str(fm[k][i][j].real) + '\n')
file2.write('\n')
for k in range(
m
): # We go through the selection and write the resulting selection of unitary matrices to a file
for i in range(n):
for j in range(n):
file2.write(str(um[k][i][j].real) + '\n')
file2.write(str(um[k][i][j].imag) + '\n')
file2.write('\n')
file2.close()
print('Training dataset successfully loaded to file')
def trainer(file_name1,
file_name2,
file_name3,
n,
m,
mini_batch_size,
counts_of_epochs,
func,
derivative_func,
functional,
coeff,
noisy_f,
noisy_u,
network,
method='L-BFGS-B'):
fm, um = load_data(n, m, file_name2) # Got the whole sample
for u in um:
fm = fm + noisy_f * np.random.randn(n, n)
um = um + noisy_u * (np.random.randn(n, n) +
1j * np.random.randn(n, n))
um = polar_correct(um) # Unitarization of basis matrices
# network = Network(n, m, mini_batch_size, file_name3) # Created an object of class Network
if coeff is not None:
list_goal_u = load_goal_matrices(n, file_name1)
# Downloaded the list of correct unitary matrices to facilitate the search
list_u = get_list_noisy(list_goal_u, coeff, n)
network.list_U = list_u # Facilitating the search for a solution with large values of n
steps = []
results = []
cross_validation = []
norma = []
x0 = transform_to_1d_list(network.list_U,
n) # Initialized Optimization algorithm
list_goal_u = load_goal_matrices(n, file_name1)
if method == 'L-BFGS-B':
print('Turned on L-BFGS-B')
for i in range(counts_of_epochs):
mini_batch_f, mini_batch_u = create_mini_batch(
n, m, mini_batch_size, fm, um)
# Formed a mini-package for Learning at one step
steps.append(i)
results.append(func(x0, network, mini_batch_f, mini_batch_u, n))
f = get_random_phase(n)
cross_validation.append(
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
norma.append(
norma_square(
interferometer(create_list_fl(mini_batch_f[0], n),
network.list_U, n), n))
res = minimize(func,
x0,
args=(network, mini_batch_f, mini_batch_u, n),
method='L-BFGS-B',
jac=derivative_func,
options={
'disp': False,
'maxiter': 1
}) # Optimization step 'BFGS'
network.list_U = transform_to_matrix(
res.x, n) # Updated the neural network
network.polar_correct()
x0 = res.x
f = get_random_phase(n)
print(
'Epoch: ', i + 1, ' Training set: ', results[i], ' Test set: ',
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
if method == 'SGD':
print('Turned on SGD')
# rate_learning = 0.1 # The best
rate_learning = 0.2
for i in range(counts_of_epochs):
mini_batch_f, mini_batch_u = create_mini_batch(
n, m, mini_batch_size, fm, um)
# Formed a mini-package for Learning at one step
steps.append(i)
results.append(func(x0, network, mini_batch_f, mini_batch_u, n))
f = get_random_phase(n)
cross_validation.append(
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
norma.append(
norma_square(
interferometer(create_list_fl(mini_batch_f[0], n),
network.list_U, n), n))
x0 = x0 - rate_learning * derivative_func(
x0, network, mini_batch_f, mini_batch_u, n)
# Optimization step 'SGD'
network.list_U = transform_to_matrix(
x0, n) # Updated the neural network
network.polar_correct()
# print(norma_square(network.list_U[0], network.N))
# print(x0, ' ', results[i])
f = get_random_phase(n)
print(
'epoch: ', i + 1, ' Training set: ', results[i], ' Test set: ',
functional(
interferometer(create_list_fl(f, n), network.list_U, n)))
# Cross validation
list_goal_u = load_goal_matrices(n, file_name1)
for i in range(10):
f = get_random_phase(n)
print(
frobenius_reduced(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
infidelity(interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
weak_reduced(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
sst(interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
steps = np.array(steps)
results = np.array(results)
cross_validation = np.array(cross_validation)
norma = np.array(norma)
error = 0.0
for i in range(1000):
f = get_random_phase(n)
error = error + infidelity(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n))
error = error / 1000
return steps, results, cross_validation, norma, error
def forward(self):
fl = create_list_fl(self.F, self.N)
_u = interferometer(fl, self.list_U, self.N)
return _u
def grad_sst(self, mini_batch_f, mini_batch_u):
# This class method calculates the gradient self.grad_U
for l in range(self.N):
self.grad_U[l] = np.zeros((self.N, self.N), dtype=complex)
# We occupy a gradient for the subsequent calculation of the sum
# Calculate self.list_A and self.list_B
for k in range(self.mini_batch_size):
fl = create_list_fl(mini_batch_f[k], self.N)
self.list_A[k][self.N - 1] = fl[self.N]
for l in range(self.N - 2, -1, -1):
self.list_A[k][l] = np.dot(self.list_A[k][l + 1], np.dot(self.list_U[l + 1], fl[l + 1]))
self.list_B[k][0] = fl[0]
for l in range(1, self.N, 1):
self.list_B[k][l] = np.dot(np.dot(fl[l], self.list_U[l - 1]), self.list_B[k][l - 1])
u_target = mini_batch_u[k]
u_result = interferometer(fl, self.list_U, self.N)
r_l, r_r = r_r_r_l(u_result)
u_1 = transform_sst(u_result)
target_1 = transform_sst(u_target)
for l in range(self.N):
for p in range(self.N):
for t in range(self.N):
a = self.list_A[k][l]
b = self.list_B[k][l]
d = self.D[p][t]
grad_u_x = np.dot(a, np.dot(d, b))
grad_u_y = 1j * np.dot(a, np.dot(d, b))
grad_r_l_x = np.eye(self.N, dtype=complex)
grad_r_r_x = np.eye(self.N, dtype=complex)
grad_r_l_y = np.eye(self.N, dtype=complex)
grad_r_r_y = np.eye(self.N, dtype=complex)
for i in range(self.N):
# grad_r_l_x[i][i] = grad_u_x[i][0].conjugate()
# grad_r_l_y[i][i] = grad_u_y[i][0].conjugate()
grad_r_l_x[i][i] = (1j * ((u_result[i][0].conjugate() /
abs(u_result[i][0])) * grad_u_x[i][0]).imag /
u_result[i][0].conjugate()).conjugate()
grad_r_l_y[i][i] = (1j * ((u_result[i][0].conjugate() /
abs(u_result[i][0])) * grad_u_y[i][0]).imag /
u_result[i][0].conjugate()).conjugate()
if i == 0:
grad_r_r_x[i][i] = 0.0
grad_r_r_y[i][i] = 0.0
else:
# grad_r_r_x[i][i] = grad_u_x[0][i].conjugate()
# grad_r_r_y[i][i] = grad_u_y[0][i].conjugate()
grad_r_r_x[i][i] = (1j * ((u_result[0][i].conjugate() /
abs(u_result[0][i])) * grad_u_x[0][i]).imag /
u_result[0][i].conjugate()).conjugate()
grad_r_r_y[i][i] = (1j * ((u_result[0][i].conjugate() /
abs(u_result[0][i])) * grad_u_y[0][i]).imag /
u_result[0][i].conjugate()).conjugate()
grad_v_x = np.dot(grad_r_l_x, np.dot(u_result, r_r)) + np.dot(r_l, np.dot(grad_u_x, r_r)) + \
np.dot(r_l, np.dot(u_result, grad_r_r_x))
grad_v_y = np.dot(grad_r_l_y, np.dot(u_result, r_r)) + np.dot(r_l, np.dot(grad_u_y, r_r)) + \
np.dot(r_l, np.dot(u_result, grad_r_r_y))
self.grad_U[l][p][t] += (2 / self.N) * np.sum((u_1 - target_1).conj() * grad_v_x).real + \
1j * (2 / self.N) * np.sum((u_1 - target_1).conj() * grad_v_y).real
for l in range(self.N):
self.grad_U[l] = self.grad_U[l] / self.mini_batch_size # Average the gradient over the mini-packet