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A python 3 program that print the operation matrix for a CROT operation from the paper at . The printed operation is just a controlled quarter-turn around the Y axis.
# -*- coding: utf-8 -*-
import numpy as np
import math
import cmath
np.set_printoptions(precision=2, suppress=True)
qubit_count = 2
σx = np.mat([[0, 1], [1, 0]])
σy = np.mat([[0, -1j], [1j, 0]])
σz = np.mat([[1, 0], [0, -1]])
π = math.pi
def CROT(c, t, a, θ):
return R(t, θ/2) * Rc(c, t, a, θ/2, True)
def Rc(c, t, a, θ, on):
mexp = matrix_lift(lambda λ: cmath.exp((-1 if on else 1) * λ * 1j * θ / 2))
op = expand(σy, t)*expand(a, c)
return mexp(op)
def R(t, θ):
mexp = matrix_lift(lambda λ: cmath.exp(λ * 1j * θ / 2))
op = expand(σy, t)
return mexp(op)
def expand(qubit_op_matrix, qubit_index):
Expands a single-qubit operation to apply to one of the qubits amongst several.
post = np.identity(1 << qubit_index)
pre = np.identity(1 << (qubit_count - qubit_index - 1))
return np.kron(np.kron(pre, qubit_op_matrix), post)
def matrix_lift(f):
Lifts a function so it modifies matrices instead of numbers, by taking the
spectral decomposition of the matrix and using the function to transform
its eigenvalues.
def matrix_lift_helper(m):
w, v = np.linalg.eig(m)
result = np.mat(np.zeros(m.shape, np.complex128))
for i in range(len(w)):
eigen_val = w[i]
eigen_vec = np.mat(v[:, i])
eigen_mat =, eigen_vec.H)
result += f(eigen_val) * eigen_mat
return result
return lambda m: matrix_lift_helper(m)
print(CROT(1, 0, σz, π/2))
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