notsure.version2.py

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

# Define the initial quantum circuit with 2 qubits
def create_initial_circuit():
    qc = QuantumCircuit(2)
    qc.h(0)  # Apply Hadamard gate to the first qubit
    qc.cx(0, 1)  # Apply CNOT gate with control qubit 0 and target qubit 1
    return qc

# Define the folding operation to increase the number of qubits to 127
def apply_folding_mechanic(qc, num_folds):
    # Add additional qubits for each fold
    for i in range(num_folds):
        qc.add_register(QuantumRegister(2, f'q{i+1}'))
        # Apply controlled rotation gates to create entanglement
        qc.crz(0.5, 2*i, 2*i+2)
        qc.crz(0.5, 2*i+1, 2*i+3)
        # Apply additional gates for demonstration
        qc.h(2*i+2)
        qc.cx(2*i+2, 2*i+3)
    return qc

# 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

# Define a noise level adjuster
class NoiseAdjuster:
    def __init__(self, min_noise, max_noise, step):
        self.min_noise = min_noise
        self.max_noise = max_noise
        self.step = step
        self.noise_level = min_noise

    def adjust(self, feedback):
        if feedback > 0.1:  # If the difference is too large, decrease the noise level
            self.noise_level -= self.step
        elif feedback < 0.01:  # If the difference is small, increase the noise level
            self.noise_level += self.step
        self.noise_level = np.clip(self.noise_level, self.min_noise, self.max_noise)
        return self.noise_level

# Create the initial quantum circuit
initial_qc = create_initial_circuit()

# Apply the folding operation to increase the number of qubits to 127
num_folds = 62  # Number of times to apply the folding operation
folded_qc = apply_folding_mechanic(initial_qc, num_folds)

# Add a simple bit-flip error correction code
for i in range(0, 124, 3):  # Adjusted range to avoid IndexError
    folded_qc.cx(i, i+1)
    folded_qc.cx(i, i+2)
    folded_qc.ccx(i+1, i+2, i)

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

# Create a noise adjuster
noise_adjuster = NoiseAdjuster(0, 0.1, 0.01)

# Run the quantum circuit with variable noise levels
noisy_statevectors = []
for _ in range(100):  # Run the circuit 100 times
    noise_level = noise_adjuster.noise_level
    noise_model = get_noise_model(noise_level, 2*noise_level)
    job = simulator.run(transpile(folded_qc, simulator), noise_model=noise_model)
    noisy_statevector = job.result().get_statevector()
    noisy_statevectors.append(noisy_statevector)

    # Calculate the difference between the noisy statevector and the ideal statevector
    difference = np.linalg.norm(noisy_statevector - statevector)
    
    # Adjust the noise level based on the feedback
    noise_adjuster.adjust(difference)