find eigenvalue & eigenvector

import numpy as np
sigmay = np.array([[0,-1j],[1j,0]])
print(sigmay)
eigenvalues, eigenvectors  = np.linalg.eig(sigmay)
print(eigenvalues)
print(eigenvectors)

vector operation

import numpy as np
v1 = np.array([[1j],[2]])
print(v1)

print(v1.shape)

v2 = v1/np.linalg.norm(v1)
print(v2)
print(np.vdot(v2,v2))

Normaliztion

import numpy as np
v1 = np.array([[1],[1]])
print(v1)
from sklearn.preprocessing import normalize
v2 = v1/np.linalg.norm(v1)
print(v2)

inner product

import numpy as np

v1=np.array([[np.sqrt(3)],[1]])
v2 = np.array([[1],[2]])
inner_result=np.vdot(v1,v2)
print(inner_result)

print(v1.shape)
print(v2.shape)