hack.the.planet.quantum.color.interpolation.log

import matplotlib.pyplot as plt
import numpy as np
from qiskit import QuantumCircuit, transpile, assemble, Aer
from qiskit.providers.aer.noise import NoiseModel
from qiskit.providers.aer.noise.errors import pauli_error

# Create a quantum circuit
qc = QuantumCircuit(1)
qc.h(0)

# Simulate the quantum state
simulator = Aer.get_backend('statevector_simulator')
job = simulator.run(transpile(qc, simulator))
statevector = job.result().get_statevector()

# Define the noise model
def get_noise_model(prob_1, prob_2):
    error_1 = pauli_error([('X', prob_1), ('I', 1 - prob_1)])
    error_2 = pauli_error([('XX', prob_2), ('II', 1 - prob_2)])
    noise_model = NoiseModel()
    noise_model.add_all_qubit_quantum_error(error_1, ['h'])
    noise_model.add_all_qubit_quantum_error(error_2, ['cx'])
    return noise_model

# Run the quantum circuit multiple times with different levels of noise
noise_levels = np.linspace(0, 0.1, 10)  # 10 different noise levels
noisy_statevectors = []
for noise_level in noise_levels:
    noise_model = get_noise_model(noise_level, 2*noise_level)
    job = simulator.run(transpile(qc, simulator), noise_model=noise_model)
    noisy_statevector = job.result().get_statevector()
    noisy_statevectors.append(noisy_statevector)

# Convert the statevectors to real numbers and map them to colors
values = [np.linalg.norm(sv) for sv in noisy_statevectors]
colors = [cmap(value) for value in values]

# Interpolate between the colors
# For simplicity, let's just take the average of the colors
average_color = np.mean(colors, axis=0)

# Display the original color, the noisy colors, and the average color
color_code_matrix = np.array([cmap(np.linalg.norm(statevector))] + colors + [average_color])
plt.imshow(color_code_matrix, aspect='auto')
plt.show()