For the first layer of your circuit, compute the tensor product between the unitary matrix of the CNOT gate and the identity matrix (using numpy's kron()). Do a similar operation for the second layer. You will obtain two 8x8 matrices. Then multiply them using numpy's matmul().

Here you have the working code:

import numpy as np

swapcnot = np.array([[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]])

layer1 = np.kron(np.eye(2),swapcnot )

layer2 = np.kron( swapcnot, np.eye(2) )

print( np.matmul(layer2,layer1) )

Output:

[[1. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1.]
[0. 0. 1. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0. 0. 0.]]

For the first layer of your circuit, compute the tensor product between the unitary matrix of the (swapped) CNOT gate and the identity matrix (using numpy's kron()). Do a similar operation for the second layer. You will obtain two 8x8 matrices. Then multiply them using numpy's matmul().

Here you have the working code:

import numpy as np

swapcnot = np.array([[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]])

layer1 = np.kron(np.eye(2),swapcnot )

layer2 = np.kron( swapcnot, np.eye(2) )

print( np.matmul(layer2,layer1) )

Output:

[[1. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1.]
[0. 0. 1. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0. 0. 0.]]

For the first layer of your circuit, compute the tensor product between the unitary matrix of the CNOT gate and the identity matrix (using numpy's kron()). Do a similar operation for the second layer. You will obtain two 8x8 matrices. Then multiply them using numpy's matmul().

For the first layer of your circuit, compute the tensor product between the unitary matrix of the CNOT gate and the identity matrix (using numpy's kron()). Do a similar operation for the second layer. You will obtain two 8x8 matrices. Then multiply them using numpy's matmul().

Here you have the working code:

import numpy as np

swapcnot = np.array([[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]])

layer1 = np.kron(np.eye(2),swapcnot )

layer2 = np.kron( swapcnot, np.eye(2) )

print( np.matmul(layer2,layer1) )

Output:

[[1. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1.]
[0. 0. 1. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0. 0. 0.]]