def __init__(self, herm, system_size):
self.size = system_size
self.herm = herm
self.alpha = np.zeros((self.size, len(self.herm)))
self.beta = np.zeros((self.size, len(self.herm)))
self._init_params()
self.optimizer_psi = MomentumOptimizer()
self.optimizer_phi = MomentumOptimizer()
def __init__(self, system_size):
self.size = system_size
self.qc = self.init_qcircuit()
self.optimizer = MomentumOptimizer()
class Generator:
def __init__(self, system_size):
self.size = system_size
self.qc = self.init_qcircuit()
self.optimizer = MomentumOptimizer()
def set_qcircuit(self, qc):
self.qc = qc
def init_qcircuit(self):
qcircuit = Quantum_Circuit(self.size, "generator")
return qcircuit
def getGen(self):
return np.kron(self.qc.get_mat_rep(), Identity(self.size))
def _grad_theta(self, dis, real_state):
G = self.getGen()
phi = dis.getPhi()
psi = dis.getPsi()
fake_state = np.matmul(G, input_state)
try:
A = expm((-1 / lamb) * phi)
except Exception:
print('grad_gen -1/lamb:\n', (-1 / lamb))
print('size of phi:\n', phi.shape)
try:
B = expm((1 / lamb) * psi)
except Exception:
print('grad_gen 1/lamb:\n', (1 / lamb))
print('size of psi:\n', psi.shape)
grad_g_psi = list()
grad_g_phi = list()
grad_g_reg = list()
for i in range(self.qc.depth):
grad_i = np.kron(self.qc.get_grad_mat_rep(i), Identity(system_size))
# for psi term
grad_g_psi.append(0)
# for phi term
fake_grad = np.matmul(grad_i, input_state)
tmp_grad = np.matmul(fake_grad.getH(), np.matmul(phi, fake_state)) + np.matmul(fake_state.getH(),np.matmul(phi, fake_grad))
grad_g_phi.append(np.asscalar(tmp_grad))
# for reg term
term1 = np.matmul(fake_grad.getH(), np.matmul(A, fake_state)) * np.matmul(real_state.getH(),np.matmul(B, real_state))
term2 = np.matmul(fake_state.getH(), np.matmul(A, fake_grad)) * np.matmul(real_state.getH(),np.matmul(B, real_state))
term3 = np.matmul(fake_grad.getH(), np.matmul(B, real_state)) * np.matmul(real_state.getH(),np.matmul(A, fake_state))
term4 = np.matmul(fake_state.getH(), np.matmul(B, real_state)) * np.matmul(real_state.getH(),np.matmul(A, fake_grad))
term5 = np.matmul(fake_grad.getH(), np.matmul(A, real_state)) * np.matmul(real_state.getH(),np.matmul(B, fake_state))
term6 = np.matmul(fake_state.getH(), np.matmul(A, real_state)) * np.matmul(real_state.getH(),np.matmul(B, fake_grad))
term7 = np.matmul(fake_grad.getH(), np.matmul(B, fake_state)) * np.matmul(real_state.getH(),np.matmul(A, real_state))
term8 = np.matmul(fake_state.getH(), np.matmul(B, fake_grad)) * np.matmul(real_state.getH(),np.matmul(A, real_state))
tmp_reg_grad = lamb / np.e * (
cst1 * (term1 + term2) - cst2 * (term3 + term4) - cst2 * (term5 + term6) + cst3 * (term7 + term8))
grad_g_reg.append(np.asscalar(tmp_reg_grad))
g_psi = np.asarray(grad_g_psi)
g_phi = np.asarray(grad_g_phi)
g_reg = np.asarray(grad_g_reg)
grad = np.real(g_psi - g_phi - g_reg)
return grad
def update_gen(self, dis, real_state):
theta = []
for gate in self.qc.gates:
theta.append(gate.angle)
grad = np.asarray(self._grad_theta(dis, real_state))
theta = np.asarray(theta)
new_angle = self.optimizer.compute_grad(theta,grad,'min')
for i in range(self.qc.depth):
self.qc.gates[i].angle = new_angle[i]
class Discriminator:
def __init__(self, herm, system_size):
self.size = system_size
self.herm = herm
self.alpha = np.zeros((self.size, len(self.herm)))
self.beta = np.zeros((self.size, len(self.herm)))
self._init_params()
self.optimizer_psi = MomentumOptimizer()
self.optimizer_phi = MomentumOptimizer()
def _init_params(self):
# Discriminator Parameters
for i in range(self.size):
self.alpha[i] = -1 + 2 * np.random.random(len(self.herm))
self.beta[i] = -1 + 2 * np.random.random(len(self.herm))
def getPsi(self):
'''
get matrix representation of real part of discriminator
:param alpha:
parameters of psi(ndarray):size = [num_qubit, 4]
0: I
1: X
2: Y
3: Z
:return:
'''
psi = 1
for i in range(self.size):
psi_i = np.zeros_like(self.herm[0], dtype=complex)
for j in range(len(self.herm)):
psi_i += self.alpha[i][j] * self.herm[j]
psi = np.kron(psi, psi_i)
return psi
def getPhi(self):
'''
get matrix representation of fake part of discriminator
:param beta:
parameters of psi(ndarray):size = [num_qubit, 4]
0: I
1: X
2: Y
3: Z
:return:
'''
phi = 1
for i in range(self.size):
phi_i = np.zeros_like(self.herm[0], dtype=complex)
for j in range(len(self.herm)):
phi_i += self.beta[i][j] * self.herm[j]
phi = np.kron(phi, phi_i)
return phi
# Psi gradients
def _grad_psi(self, type):
grad_psi = list()
for i in range(self.size):
grad_psiI = 1
for j in range(self.size):
if i == j:
grad_psii = self.herm[type]
else:
grad_psii = np.zeros_like(self.herm[0], dtype=complex)
for k in range(len(self.herm)):
grad_psii += self.alpha[j][k] * self.herm[k]
grad_psiI = np.kron(grad_psiI, grad_psii)
grad_psi.append(grad_psiI)
return grad_psi
def _grad_alpha(self, gen, real_state):
G = gen.getGen()
psi = self.getPsi()
phi = self.getPhi()
fake_state = np.matmul(G, input_state)
try:
A = expm((-1 / lamb) * phi)
except Exception:
print('grad_alpha -1/lamb:\n', (-1 / lamb))
print('size of phi:\n', phi.shape)
try:
B = expm((1 / lamb) * psi)
except Exception:
print('grad_alpha 1/lamb:\n', (1 / lamb))
print('size of psi:\n', psi.shape)
cs = 1 / lamb
grad_psi_term = np.zeros_like(self.alpha, dtype=complex)
grad_phi_term = np.zeros_like(self.alpha, dtype=complex)
grad_reg_term = np.zeros_like(self.alpha, dtype=complex)
for type in range(len(self.herm)):
gradpsi = self._grad_psi(type)
gradpsi_list = list()
gradphi_list = list()
gradreg_list = list()
for grad_psi in gradpsi:
gradpsi_list.append(np.asscalar(np.matmul(real_state.getH(), np.matmul(grad_psi, real_state))))
gradphi_list.append(0)
term1 = cs * np.matmul(fake_state.getH(), np.matmul(A, fake_state)) * np.matmul(real_state.getH(),np.matmul(grad_psi,np.matmul(B,real_state)))
term2 = cs * np.matmul(fake_state.getH(), np.matmul(grad_psi, np.matmul(B, real_state))) * np.matmul(real_state.getH(), np.matmul(A, fake_state))
term3 = cs * np.matmul(fake_state.getH(), np.matmul(A, real_state)) * np.matmul(real_state.getH(),np.matmul(grad_psi,np.matmul(B,fake_state)))
term4 = cs * np.matmul(fake_state.getH(), np.matmul(grad_psi, np.matmul(B, fake_state))) * np.matmul(real_state.getH(), np.matmul(A, real_state))
gradreg_list.append(np.asscalar(lamb / np.e * (cst1 * term1 - cst2 * term2 - cst2 * term3 + cst3 * term4)))
# calculate grad of psi term
grad_psi_term[:, type] = np.asarray(gradpsi_list)
# calculate grad of phi term
grad_phi_term[:, type] = np.asarray(gradphi_list)
# calculate grad of reg term
grad_reg_term[:, type] = np.asarray(gradreg_list)
grad = np.real(grad_psi_term - grad_phi_term - grad_reg_term)
return grad
# Phi gradients
def _grad_phi(self, type):
grad_phi = list()
for i in range(self.size):
grad_phiI = 1
for j in range(self.size):
if i == j:
grad_phii = self.herm[type]
else:
grad_phii = np.zeros_like(self.herm[0], dtype=complex)
for k in range(len(self.herm)):
grad_phii += self.beta[j][k] * self.herm[k]
grad_phiI = np.kron(grad_phiI, grad_phii)
grad_phi.append(grad_phiI)
return grad_phi
def _grad_beta(self, gen, real_state):
G = gen.getGen()
psi = self.getPsi()
phi = self.getPhi()
fake_state = np.matmul(G, input_state)
try:
A = expm((-1 / lamb) * phi)
except Exception:
print('grad_beta -1/lamb:\n', (-1 / lamb))
print('size of phi:\n', phi.shape)
try:
B = expm((1 / lamb) * psi)
except Exception:
print('grad_beta 1/lamb:\n', (1 / lamb))
print('size of psi:\n', psi.shape)
cs = -1 / lamb
grad_psi_term = np.zeros_like(self.beta, dtype=complex)
grad_phi_term = np.zeros_like(self.beta, dtype=complex)
grad_reg_term = np.zeros_like(self.beta, dtype=complex)
for type in range(len(self.herm)):
gradphi = self._grad_phi(type)
gradpsi_list = list()
gradphi_list = list()
gradreg_list = list()
for grad_phi in gradphi:
gradpsi_list.append(0)
gradphi_list.append(np.asscalar(np.matmul(fake_state.getH(), np.matmul(grad_phi, fake_state))))
term1 = cs * np.matmul(fake_state.getH(), np.matmul(grad_phi, np.matmul(A, fake_state))) * np.matmul(real_state.getH(), np.matmul(B, real_state))
term2 = cs * np.matmul(fake_state.getH(), np.matmul(B, real_state)) * np.matmul(real_state.getH(),np.matmul(grad_phi,np.matmul(A,fake_state)))
term3 = cs * np.matmul(fake_state.getH(), np.matmul(grad_phi, np.matmul(A, real_state))) * np.matmul(real_state.getH(), np.matmul(B, fake_state))
term4 = cs * np.matmul(fake_state.getH(), np.matmul(B, fake_state)) * np.matmul(real_state.getH(),np.matmul(grad_phi,np.matmul(A,real_state)))
gradreg_list.append(np.asscalar(lamb / np.e * (cst1 * term1 - cst2 * term2 - cst2 * term3 + cst3 * term4)))
# calculate grad of psi term
grad_psi_term[:, type] = np.asarray(gradpsi_list)
# calculate grad of phi term
grad_phi_term[:, type] = np.asarray(gradphi_list)
# calculate grad of reg term
grad_reg_term[:, type] = np.asarray(gradreg_list)
grad = np.real(grad_psi_term - grad_phi_term - grad_reg_term)
return grad
def update_dis(self, gen, real_state):
grad_alpha = self._grad_alpha(gen, real_state)
# update alpha
new_alpha = self.optimizer_psi.compute_grad(self.alpha, grad_alpha, 'max')
# new_alpha = self.alpha + eta * self._grad_alpha(gen)
grad_beta = self._grad_beta(gen, real_state)
# update beta
new_beta = self.optimizer_phi.compute_grad(self.beta, grad_beta, 'max')
# new_beta = self.beta + eta * self._grad_beta(gen)
self.alpha = new_alpha
self.beta = new_beta
class Generator:
def __init__(self, system_size):
self.size = system_size
self.qc = self.init_qcircuit()
self.optimizer = MomentumOptimizer()
def single_layer(self, qc):
for i in range(self.size):
# qc.add_gate(Quantum_Gate("X", i, angle=0.5000 * np.pi))
# qc.add_gate(Quantum_Gate("Y", i, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("Z", i, angle=0.5000 * np.pi))
return qc
def entanglement_layer(self, qc):
for i in range(self.size):
for j in range(i + 1, self.size):
# print(i,j)
qc.add_gate(Quantum_Gate("XX", i, j, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("YY", i, j, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("ZZ", i, j, angle=0.5000 * np.pi))
print(i, j)
return qc
def entangle_adjacent_layer(self, qc, type):
for i in range(self.size - 1):
qc.add_gate(Quantum_Gate(type, i, i + 1, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate(type, 0, self.size - 1, angle=0.5000 * np.pi))
return qc
def set_qcircuit(self, qc):
# qc_tmp = qc
# for i in range(4):
# qc_tmp = self.single_layer(qc_tmp)
# qc_tmp = self.entanglement_layer(qc_tmp)
# qc = qc_tmp
# for j in range(layer):
# for i in range(self.size):
# qc.add_gate(Quantum_Gate("Z", i, angle=0.5000 * np.pi))
# if i < self.size:
# qc = self.entangle_adjacent_layer(qc, "XX")
# qc = self.entangle_adjacent_layer(qc, "YY")
# qc = self.entangle_adjacent_layer(qc, "ZZ")
# qc.add_gate(Quantum_Gate("G",None,angle=0.5000 * np.pi))
entg_list = ["XX", "YY", "ZZ"]
for j in range(layer):
for i in range(self.size):
if i < self.size - 1:
for gate in entg_list:
qc.add_gate(
Quantum_Gate(gate, i, i + 1, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("Z", i, angle=0.5000 * np.pi))
for gate in entg_list:
qc.add_gate(
Quantum_Gate(gate, 0, self.size - 1, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("Z", i, angle=0.5000 * np.pi))
qc.add_gate(Quantum_Gate("G", None, angle=0.5000 * np.pi))
# for gate in entg_list:
# for i in range(self.size):
# if i < self.size - 1:
# qc.add_gate(Quantum_Gate(gate, i, i + 1, angle=0.5000 * np.pi))
# else:
# qc.add_gate(Quantum_Gate(gate, 0, self.size - 1, angle=0.5000 * np.pi))
# qc.add_gate(Quantum_Gate("Z", i, angle=0.5000 * np.pi))
theta = np.random.randn(len(qc.gates))
for i in range(len(qc.gates)):
qc.gates[i].angle = theta[i]
print(i + 1, qc.gates[i].name, qc.gates[i].qubit1,
qc.gates[i].qubit2)
return qc
def init_qcircuit(self):
qcircuit = Quantum_Circuit(self.size, "generator")
self.set_qcircuit(qcircuit)
return qcircuit
def getGen(self):
return np.kron(self.qc.get_mat_rep(), Identity(system_size))
def _grad_gen(self, dis):
G = self.getGen()
phi = dis.getPhi()
psi = dis.getPsi()
fake_state = np.matmul(G, zero_state)
try:
A = expm((-1 / lamb) * phi)
except Exception:
print('grad_gen -1/lamb:\n', (-1 / lamb))
print('size of phi:\n', phi.shape)
try:
B = expm((1 / lamb) * psi)
except Exception:
print('grad_gen 1/lamb:\n', (1 / lamb))
print('size of psi:\n', psi.shape)
# print("g: \n", G)
# print("phi: \n", phi)
# print("psi: \n", psi)
# phi_tmp = np.asmatrix(phi)
# print('phi:\n',phi_tmp.getH()@phi_tmp)
#
# psi_tmp = np.asmatrix(psi)
# print('psi:\n',psi_tmp.getH()@psi_tmp)
# print("expHerm:",expHerm)
grad_g_psi = list()
grad_g_phi = list()
grad_g_reg = list()
for i in range(self.qc.depth):
grad_i = np.kron(self.qc.get_grad_mat_rep(i),
Identity(system_size))
# for psi term
grad_g_psi.append(0)
# for phi term
fake_grad = np.matmul(grad_i, zero_state)
tmp_grad = np.matmul(fake_grad.getH(), np.matmul(
phi, fake_state)) + np.matmul(fake_state.getH(),
np.matmul(phi, fake_grad))
grad_g_phi.append(np.asscalar(tmp_grad))
# for reg term
term1 = np.matmul(fake_grad.getH(), np.matmul(
A, fake_state)) * np.matmul(real_state.getH(),
np.matmul(B, real_state))
term2 = np.matmul(fake_state.getH(), np.matmul(
A, fake_grad)) * np.matmul(real_state.getH(),
np.matmul(B, real_state))
term3 = np.matmul(fake_grad.getH(), np.matmul(
B, real_state)) * np.matmul(real_state.getH(),
np.matmul(A, fake_state))
term4 = np.matmul(fake_state.getH(), np.matmul(
B, real_state)) * np.matmul(real_state.getH(),
np.matmul(A, fake_grad))
term5 = np.matmul(fake_grad.getH(), np.matmul(
A, real_state)) * np.matmul(real_state.getH(),
np.matmul(B, fake_state))
term6 = np.matmul(fake_state.getH(), np.matmul(
A, real_state)) * np.matmul(real_state.getH(),
np.matmul(B, fake_grad))
term7 = np.matmul(fake_grad.getH(), np.matmul(
B, fake_state)) * np.matmul(real_state.getH(),
np.matmul(A, real_state))
term8 = np.matmul(fake_state.getH(), np.matmul(
B, fake_grad)) * np.matmul(real_state.getH(),
np.matmul(A, real_state))
tmp_reg_grad = lamb / np.e * (cst1 * (term1 + term2) - cst2 *
(term3 + term4) - cst2 *
(term5 + term6) + cst3 *
(term7 + term8))
grad_g_reg.append(np.asscalar(tmp_reg_grad))
g_psi = np.asarray(grad_g_psi)
g_phi = np.asarray(grad_g_phi)
g_reg = np.asarray(grad_g_reg)
grad = np.real(g_psi - g_phi - g_reg)
# print("grad:\n",grad)
# del grad_g,grad_g_phi,grad_g_psi,grad_g_reg,G,phi,psi,A,B,fake_grad,tmp_grad,g_phi,g_psi,g_reg,grad_i
# gc.collect()
return grad
def update_gen(self, dis):
theta = []
for gate in self.qc.gates:
theta.append(gate.angle)
grad = np.asarray(self._grad_gen(dis))
theta = np.asarray(theta)
new_angle = self.optimizer.compute_grad(theta, grad, 'min')
for i in range(self.qc.depth):
self.qc.gates[i].angle = new_angle[i]
print('gen_theta max:{} gen_theta min:{}'.format(
np.max(grad), np.min(grad)))