def __measure_parameterless(self, state, which_qubits, result_desired):
r"""进行 01 测量。
Args:
state (Tensor): 输入的量子态
which_qubits (list): 测量作用的量子比特编号
result_desired (str): 期望得到的测量结果
Returns:
Tensor: 测量坍塌后的量子态
Tensor：测量坍塌得到的概率
str: 测量得到的结果
"""
n = self.get_qubit_number()
assert len(which_qubits) == len(result_desired), \
"the length of qubits wanted to be measured and the result desired should be same"
op_list = [np.eye(2, dtype=np.complex128)] * n
for i, ele in zip(which_qubits, result_desired):
k = int(ele)
rho = np.zeros((2, 2), dtype=np.complex128)
rho[int(k), int(k)] = 1
op_list[i] = rho
if n > 1:
measure_operator = paddle.to_tensor(NKron(*op_list))
else:
measure_operator = paddle.to_tensor(op_list[0])
state_measured = matmul(matmul(measure_operator, state),
dagger(measure_operator))
prob = real(
trace(
matmul(matmul(dagger(measure_operator), measure_operator),
state)))
state_measured = divide(state_measured, prob)
return state_measured, prob, result_desired
def forward(self, H, N, N_SYS_B, beta, D):
# Apply quantum neural network onto the initial state
rho_AB = U_theta(self.initial_state, self.theta, N, D)
# Calculate the partial trace to get the state rho_B of subsystem B
rho_B = partial_trace(rho_AB, 2**(N - N_SYS_B), 2**N_SYS_B, 1)
# Calculate the three components of the loss function
rho_B_squre = matmul(rho_B, rho_B)
loss1 = paddle.real(trace(matmul(rho_B, H)))
loss2 = paddle.real(trace(rho_B_squre)) * 2 / beta
loss3 = -(paddle.real(trace(matmul(rho_B_squre, rho_B))) + 3) / (2 *
beta)
# Get the final loss function
loss = loss1 + loss2 + loss3
return loss, rho_B
def __measure_parameterized(self, state, which_qubits, result_desired,
theta):
r"""进行参数化的测量。
Args:
state (Tensor): 输入的量子态
which_qubits (list): 测量作用的量子比特编号
result_desired (str): 期望得到的测量结果
theta (Tensor): 测量运算的参数
Returns:
Tensor: 测量坍塌后的量子态
Tensor：测量坍塌得到的概率
str: 测量得到的结果
"""
n = self.get_qubit_number()
assert len(which_qubits) == len(result_desired), \
"the length of qubits wanted to be measured and the result desired should be same"
op_list = [paddle.to_tensor(np.eye(2, dtype=np.complex128))] * n
for idx in range(0, len(which_qubits)):
i = which_qubits[idx]
ele = result_desired[idx]
if int(ele) == 0:
basis0 = paddle.to_tensor(
np.array([[1, 0], [0, 0]], dtype=np.complex128))
basis1 = paddle.to_tensor(
np.array([[0, 0], [0, 1]], dtype=np.complex128))
rho0 = multiply(basis0, cos(theta[idx]))
rho1 = multiply(basis1, sin(theta[idx]))
rho = add(rho0, rho1)
op_list[i] = rho
elif int(ele) == 1:
# rho = diag(concat([cos(theta[idx]), sin(theta[idx])]))
# rho = paddle.to_tensor(rho, zeros((2, 2), dtype="float64"))
basis0 = paddle.to_tensor(
np.array([[1, 0], [0, 0]], dtype=np.complex128))
basis1 = paddle.to_tensor(
np.array([[0, 0], [0, 1]], dtype=np.complex128))
rho0 = multiply(basis0, sin(theta[idx]))
rho1 = multiply(basis1, cos(theta[idx]))
rho = add(rho0, rho1)
op_list[i] = rho
else:
print("cannot recognize the result_desired.")
# rho = paddle.to_tensor(ones((2, 2), dtype="float64"), zeros((2, 2), dtype="float64"))
measure_operator = paddle.to_tensor(op_list[0])
if n > 1:
for idx in range(1, len(op_list)):
measure_operator = kron(measure_operator, op_list[idx])
state_measured = matmul(matmul(measure_operator, state),
dagger(measure_operator))
prob = real(
trace(
matmul(matmul(dagger(measure_operator), measure_operator),
state)))
state_measured = divide(state_measured, prob)
return state_measured, prob, result_desired
def forward(self, N):
# Apply quantum neural network onto the initial state
U = U_theta(self.theta, N)
# rho_tilda is the quantum state obtained by acting U on rho, which is U*rho*U^dagger
rho_tilde = matmul(matmul(U, self.rho), dagger(U))
# Calculate loss function
loss = trace(matmul(self.sigma, rho_tilde))
return paddle.real(loss), rho_tilde
def test_paddle_backward():
## 必须每次重新构造矩阵
I = paddle.to_tensor(np.eye(2, dtype=np.float32))
U = paddle.to_tensor(
np.array([[1, -1], [1, 1]], dtype=np.float32) / np.sqrt(2))
layer = RLayer_d1()
layer.set_theta(1.)
U2 = layer(I)
loss = 1 - paddle.trace(paddle.matmul(U, paddle.transpose(U2, [1, 0]))) / 2
opt = paddle.optimizer.Adam(0.1, parameters=layer.parameters())
for epoch in range(110):
loss.backward(retain_graph=True)
opt.step()
opt.clear_grad()
if (epoch + 1) % 10 == 0:
print('epoch_%d: score=%g, theta=%g' %
(epoch, 1 - loss.numpy()[0], layer.theta.numpy()[0]))
print(layer.matrix.numpy())
def expecval(self, H):
r"""量子线路输出的量子态关于可观测量 H 的期望值。
Hint:
如果想输入的可观测量的矩阵为 :math:`0.7Z\otimes X\otimes I+0.2I\otimes Z\otimes I` 。则 ``H`` 应为 ``[[0.7, 'z0,x1'], [0.2, 'z1']]`` 。
Args:
H (list): 可观测量的相关信息
Returns:
Tensor: 量子线路输出的量子态关于 H 的期望值
代码示例:
.. code-block:: python
import numpy as np
import paddle
from paddle_quantum.circuit import UAnsatz
n = 5
H_info = [[0.1, 'x1'], [0.2, 'y0,z4']]
theta = paddle.to_tensor(np.ones(3))
cir = UAnsatz(n)
cir.rx(theta[0], 0)
cir.rz(theta[1], 1)
cir.rx(theta[2], 2)
cir.run_state_vector()
expect_value = cir.expecval(H_info).numpy()
print(f'Calculated expectation value of {H_info} is {expect_value}')
::
Calculated expectation value of [[0.1, 'x1'], [0.2, 'y0,z4']] is [-0.1682942]
"""
if self.__run_state == 'state_vector':
return real(vec_expecval(H, self.__state))
elif self.__run_state == 'density_matrix':
state = self.__state
H_mat = paddle.to_tensor(pauli_str_to_matrix(H, self.n))
return real(trace(matmul(state, H_mat)))
else:
# Raise error
raise ValueError(
"no state for measurement; please run the circuit first")
def test_paddle_param():
U = paddle.to_tensor(
np.array([[1, -1], [1, 1]], dtype=np.float32) / np.sqrt(2))
U.stop_gradient = True
theta = paddle.static.create_parameter(shape=[1, 1], dtype='float32')
cs = paddle.cos(theta / 2)
sn = paddle.sin(theta / 2)
matrix = paddle.concat([
paddle.concat([cs, -sn], axis=1),
paddle.concat([sn, cs], axis=1),
],
axis=0)
loss = 1 - paddle.trace(paddle.matmul(U, paddle.transpose(matrix,
[1, 0]))) / 2
# I = paddle.to_tensor(np.eye(2, dtype=np.float32))
dt = paddle.grad(outputs=[loss],
inputs=[theta],
create_graph=False,
retain_graph=True)[0]
print(dt)
def trace(x, offset=0, dim1=0, dim2=1, out=None):
return Tensor(paddle.trace(x, offset, dim1, dim2, out))
