def run_density_matrix(self, input_state=None, store_state=True):
r"""运行当前的量子线路，输入输出的形式为密度矩阵。
Args:
input_state (ComplexVariable, optional): 输入的密度矩阵，默认为 :math:`|00...0\rangle \langle00...0|`
store_state (bool, optional): 是否存储输出的密度矩阵，默认为 ``True`` ，即存储
Returns:
ComplexVariable: 量子线路输出的密度矩阵
代码示例:
.. code-block:: python
import numpy as np
from paddle import fluid
from paddle_quantum.circuit import UAnsatz
n = 1
theta = np.ones(3)
input_state = np.diag(np.arange(2**n))+0j
input_state = input_state / np.trace(input_state)
with fluid.dygraph.guard():
input_state_var = fluid.dygraph.to_variable(input_state)
theta = fluid.dygraph.to_variable(theta)
cir = UAnsatz(n)
cir.rx(theta[0], 0)
cir.ry(theta[1], 0)
cir.rz(theta[2], 0)
density = cir.run_density_matrix(input_state_var).numpy()
print(f"密度矩阵是\n{density}")
::
密度矩阵是
[[ 0.35403671+0.j -0.47686058-0.03603751j]
[-0.47686058+0.03603751j 0.64596329+0.j ]]
"""
state = dygraph.to_variable(density_op(
self.n)) if input_state is None else input_state
assert state.real.shape == [2**self.n,
2**self.n], "The dimension is not right"
state = matmul(self.U, matmul(state, dagger(self.U)))
if store_state:
self.__state = state
# Add info about which function user called
self.__run_state = 'density_matrix'
return state
def __init__(self,
N,
shape,
param_attr=paddle.nn.initializer.Uniform(low=0.0,
high=2 * PI),
dtype='float64'):
super(Net, self).__init__()
# Initialize theta by sampling from a uniform distribution [0, 2*pi]
self.theta = self.create_parameter(shape=shape,
attr=param_attr,
dtype=dtype,
is_bias=False)
# Set the initial state as rho = |0..0><0..0|
self.initial_state = paddle.to_tensor(density_op(N))
def run_density_matrix(self, input_state=None, store_state=True):
r"""运行当前的量子线路，输入输出的形式为密度矩阵。
Args:
input_state (Tensor, optional): 输入的密度矩阵，默认为 :math:`|00...0\rangle \langle00...0|`
store_state (bool, optional): 是否存储输出的密度矩阵，默认为 ``True`` ，即存储
Returns:
Tensor: 量子线路输出的密度矩阵
代码示例:
.. code-block:: python
import numpy as np
import paddle
from paddle_quantum.circuit import UAnsatz
from paddle_quantum.state import density_op
n = 1
theta = np.ones(3)
input_state = paddle.to_tensor(density_op(n))
theta = paddle.to_tensor(theta)
cir = UAnsatz(n)
cir.rx(theta[0], 0)
cir.ry(theta[1], 0)
cir.rz(theta[2], 0)
density_matrix = cir.run_density_matrix(input_state).numpy()
print(f"The output density matrix is\n{density_matrix}")
::
The output density matrix is
[[0.64596329+0.j 0.47686058+0.03603751j]
[0.47686058-0.03603751j 0.35403671+0.j ]]
"""
state = paddle.to_tensor(density_op(
self.n)) if input_state is None else input_state
assert paddle.real(state).shape == [2**self.n, 2**self.n
], "The dimension is not right"
state = matmul(self.U, matmul(state, dagger(self.U)))
if store_state:
self.__state = state
# Add info about which function user called
self.__run_state = 'density_matrix'
return state
def __init__(self,
N,
shape,
param_attr=fluid.initializer.Uniform(low=0.0,
high=2 * PI,
seed=SEED),
dtype='float64'):
super(Net, self).__init__()
# 初始化 theta 参数列表，并用 [0, 2*pi] 的均匀分布来填充初始值
self.theta = self.create_parameter(shape=shape,
attr=param_attr,
dtype=dtype,
is_bias=False)
# 初始化 rho = |0..0><0..0| 的密度矩阵
self.initial_state = fluid.dygraph.to_variable(density_op(N))