import svect as sv
import numpy as np
from math import pi
import qneural as qn
from matplotlib import pyplot as plt
# =============================================================================
# Entropy Dynamics for a Unitary Quantum Neural Map
# =============================================================================
# =============================================================================
# Part 1 - Preparation
# =============================================================================
# Setup the initial operator to get the initial density
U0 = sv.tensorProd([sv.WHGate(), sv.WHGate()])
# Initialize the network
Net, mult_2 = qn.initialize_network(num_neurons=2,
initial_operator=U0,
type_initial='Unitary',
return_multipliers=True)
print("\nInitial Density")
print(Net.rho)
print("\nLocal Operators")
print("\nNeuron 0")
print(Net.local_operators[0])
import svect as sv
import numpy as np
# Main Operators Used in the Circuit:
H = sv.WHGate() # Walsh-Haddamard transform
I = sv.unit() # Unit gate
X = sv.PauliX() # Pauli X
P0 = sv.proj2x2(False) # Projector P0 = |0><0|
P1 = sv.proj2x2(True) # projector P1 = |1><1|
# Circuit Operator
UCircuit = np.dot(
sv.tensorProd([P0, I]) + sv.tensorProd([P1, X]), sv.tensorProd([H, I]))
# Basis:
basis = sv.basisDef(2) # we are working with a two register basis
# Initial amplitude:
psi0 = np.zeros(len(basis))
psi0[0] = 1
# Initial ket:
ket = sv.getKet(basis, psi0)
# Implementing the quatum circuit:
print("\nQUANTUM CIRCUIT SIMULATION")
print("\nInitial ket vector:")
sv.showKet(ket)
print("\nFinal ket vector:")
ket = sv.transformKet(UCircuit, ket)