def forward(self, H_C_ls):
"""
Forward propagation
"""
cir = UAnsatz(self.num_qubits)
cir.complex_entangled_layer(self.theta, self.p)
cir.run_state_vector()
loss = cir.expecval(H_C_ls)
return loss, cir
def U_theta(theta, Hamiltonian, N, D):
"""
Quantum Neural Network
"""
# Initialize the quantum neural network by the number of qubits (width of the network)
cir = UAnsatz(N)
# Use built-in template (R_y + CNOT)
cir.real_entangled_layer(theta[:D], D)
# Add a layer of R_y rotation gates
for i in range(N):
cir.ry(theta=theta[D][i][0], which_qubit=i)
# Act QNN on the default initial state |0000>
cir.run_state_vector()
# Calculate the expectation value of the given Hamiltonian
expectation_val = cir.expecval(Hamiltonian)
return expectation_val
def U_theta(theta, Hamiltonian, N, D):
"""
Quantum Neural Network
"""
# 按照量子比特数量/网络宽度初始化量子神经网络
cir = UAnsatz(N)
# 内置的 {R_y + CNOT} 电路模板
cir.real_entangled_layer(theta[:D], D)
# 铺上最后一列 R_y 旋转门
for i in range(N):
cir.ry(theta=theta[D][i][0], which_qubit=i)
# 量子神经网络作用在默认的初始态 |0000>上
cir.run_state_vector()
# 计算给定哈密顿量的期望值
expectation_val = cir.expecval(Hamiltonian)
return expectation_val