def NKron(matrix_A, matrix_B, *args):
r"""计算两个及以上的矩阵的Kronecker积。
Args:
matrix_A (numpy.ndarray): 一个矩阵
matrix_B (numpy.ndarray): 一个矩阵
*args (numpy.ndarray): 其余矩阵
Returns:
ComplexVariable: 输入矩阵的Kronecker积
.. code-block:: python
from paddle_quantum.state import density_op_random
from paddle_quantum.utils import NKron
A = density_op_random(2)
B = density_op_random(2)
C = density_op_random(2)
result = NKron(A, B, C)
``result`` 应为： :math:`A \otimes B \otimes C`
"""
return reduce(
lambda result, index: np_kron(result, index),
args,
np_kron(matrix_A, matrix_B),
)
def NKron(AMatrix, BMatrix, *args):
"""
Recursively execute kron n times. This function at least has two matrices.
:param AMatrix: First matrix
:param BMatrix: Second matrix
:param args: If have more matrix, they are delivered by this matrix
:return: The result of tensor product.
"""
return reduce(
lambda result, index: np_kron(result, index),
args,
np_kron(AMatrix, BMatrix), )
def NKron(AMatrix, BMatrix, *args):
r"""对输入的矩阵依次求克罗内克积
Args:
AMatrix (numpy.ndarray): 输入的矩阵A
BMatrix (numpy.ndarray): 输入的矩阵B
*args (numpy.ndarray): 输入的其他矩阵
Returns:
numpy.ndarray: 所求得的克罗内克积
"""
return reduce(
lambda result, index: np_kron(result, index),
args,
np_kron(AMatrix, BMatrix),
)
def kron(self, other):
'''
Return self x other, where "x" is a Kronecker product operation.
'''
if self.isnone or other.isnone or self.mode != other.mode:
raise ValueError('Incorrect input.')
res = self.copy(copy_x=False)
res.d = self.d + other.d
if self.mode == MODE_NP or (self.mode == MODE_SP
and self.type == 'vector'):
res.x = np_kron(self.x, other.x)
if self.mode == MODE_TT:
res.x = tt_kron(other.x, self.x)
res = res.round([self.tau, other.tau])
if self.mode == MODE_SP and self.type == 'matrix':
res.x = sp_kron(self.x, other.x)
return res
def triad(lhs, rhs1, rhs2, alpha):
lhs = np_add(rhs1, np_kron(alpha, rhs2))
def scale(lhs, rhs, alpha):
lhs = np_kron(alpha, rhs)