main.py

import numpy as np
from quantum_compression import (
    variational_quantum_compression,
    simulate_quantum_circuit_with_noise,
    quantum_autoencoder_compression,
    visualize_quantum_results
)
from classical_model import build_and_train_model, evaluate_model
from neural_network import build_and_train_neural_network, predict_compressibility

def main():
    # Generate sample data
    data = [0, 1, 1, 0, 1]

    # Variational Quantum Compression
    optimal_params = variational_quantum_compression(data)
    print(f"Optimal parameters for quantum compression: {optimal_params}")

    # Create and simulate quantum circuit with optimal parameters
    qc = create_variational_circuit(data)
    counts = simulate_quantum_circuit_with_noise(qc)
    print("Quantum Compression Results with Noise Mitigation:", counts)
    visualize_quantum_results(counts)

    # Machine Learning Model Training
    n_samples = 100
    np.random.seed(42)
    X_data = np.random.randint(2, size=(n_samples, len(data)))
    y_data = np.sum(X_data, axis=1)
    model = build_and_train_model(X_data, y_data)
    accuracy = evaluate_model(model, X_data, y_data)
    print(f"Accuracy without compression: {accuracy:.2f}")

    # Neural Network for Predicting Compressibility
    nn_model = build_and_train_neural_network()
    compressibility_prediction = predict_compressibility(nn_model, data)
    print(f"Predicted compressibility: {compressibility_prediction:.2f}")

    # Quantum Autoencoder Compression
    qc_autoencoder = quantum_autoencoder_compression(data)
    autoencoder_counts = simulate_quantum_circuit_with_noise(qc_autoencoder)
    print("Quantum Autoencoder Compression Results:", autoencoder_counts)
    visualize_quantum_results(autoencoder_counts)

if __name__ == "__main__":
    main()

import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score

def build_and_train_model(X, y):
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
    model = LogisticRegression()
    param_grid = {'C': [0.1, 1, 10]}
    grid_search = GridSearchCV(model, param_grid, cv=5)
    grid_search.fit(X_train, y_train)
    best_model = grid_search.best_estimator_
    return best_model

def evaluate_model(model, X, y):
    y_pred = model.predict(X)
    accuracy = accuracy_score(y, y_pred)
    return accuracy

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

def build_and_train_neural_network():
    x_train = np.array([[np.random.randint(1, 100), np.random.random()] for _ in range(100)])
    y_train = np.array([np.random.randint(0, 1) for _ in range(100)])
    scaler = StandardScaler()
    x_train_scaled = scaler.fit_transform(x_train)
    model = Sequential([
        Dense(32, activation='relu', input_shape=(2,)),
        Dense(1, activation='sigmoid')
    ])
    opt = Adam()
    model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
    x_train_split, x_val_split, y_train_split, y_val_split = train_test_split(x_train_scaled, y_train, test_size=0.2, random_state=42)
    model.fit(x_train_split, y_train_split, epochs=50, validation_data=(x_val_split, y_val_split))
    return model

def predict_compressibility(model, data):
    x_test = np.array([[len(data), np.random.random()]])
    scaler = StandardScaler()
    x_test_scaled = scaler.fit_transform(x_test)
    predictions = model.predict(x_test_scaled)
    return predictions[0]

from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
from qiskit.circuit.library import EfficientSU2
from qiskit.algorithms.optimizers import COBYLA
from qiskit.algorithms import VQE
from qiskit.utils import QuantumInstance
from qiskit.providers.aer import AerSimulator
from qiskit.visualization import plot_histogram

# Function to create a variational quantum circuit
def create_variational_circuit(data):
    num_qubits = len(data)
    feature_map = EfficientSU2(num_qubits, reps=1)
    qc = QuantumCircuit(num_qubits)
    qc.compose(feature_map, inplace=True)
    return qc

# Function to perform variational quantum compression
def variational_quantum_compression(data):
    num_qubits = len(data)
    qc = create_variational_circuit(data)
    optimizer = COBYLA(maxiter=100)
    vqe = VQE(qc, optimizer=optimizer, quantum_instance=QuantumInstance(Aer.get_backend('statevector_simulator')))
    result = vqe.compute_minimum_eigenvalue()
    return result.optimal_point

# Function to simulate quantum circuit with noise mitigation
def simulate_quantum_circuit_with_noise(qc):
    simulator = AerSimulator()
    t_qc = transpile(qc, simulator)
    job = execute(t_qc, backend=simulator, shots=1024)
    result = job.result()
    counts = result.get_counts()
    return counts

# Function to create a quantum autoencoder
def create_quantum_autoencoder(data):
    num_qubits = len(data)
    encoder = QuantumCircuit(num_qubits)
    for i in range(num_qubits):
        encoder.h(i)
        encoder.cx(i, (i + 1) % num_qubits)
    decoder = encoder.inverse()
    return encoder, decoder

# Function to perform quantum autoencoder compression
def quantum_autoencoder_compression(data):
    encoder, decoder = create_quantum_autoencoder(data)
    qc = QuantumCircuit(len(data) * 2)
    qc.compose(encoder, inplace=True)
    qc.compose(decoder, inplace=True)
    return qc

# Visualization function for quantum results
def visualize_quantum_results(counts):
    plot_histogram(counts).show()