We first generate sample data and prepare it for quantum and classical processing. The data variable contains binary data, which we use for both quantum compression and classical model training. The code snippet below demonstrates this process:
# 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}")
We simulate the quantum circuit with noise mitigation using the simulate_quantum_circuit_with_noise function. This function transpires the quantum circuit for the AerSimulator and executes it, returning the result counts.
# 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)
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()
def build_and_train_model(X, y):
"""
Build and train a logistic regression model using grid search for hyperparameter tuning.
Parameters:
X (array-like): Feature data.
y (array-like): Target data.
Returns:
best_model: Trained logistic regression model with the best parameters found by grid search.
"""
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):
"""
Evaluate the logistic regression model.
Parameters:
model: Trained logistic regression model.
X (array-like): Feature data.
y (array-like): Target data.
Returns:
accuracy (float): Accuracy of the model.
"""
y_pred = model.predict(X)
accuracy = accuracy_score(y, y_pred)
return accuracy
def build_and_train_neural_network():
"""
Build and train a simple neural network to predict compressibility.
Returns:
model: Trained neural network model.
"""
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):
"""
Predict the compressibility of the data using the trained neural network model.
Parameters:
model: Trained neural network model.
data (array-like): Input data to predict compressibility.
Returns:
prediction (float): Predicted compressibility score.
"""
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]
def create_variational_circuit(data):
"""
Create a variational quantum circuit for the given data.
Parameters:
data (array-like): Input data for the quantum circuit.
Returns:
qc (QuantumCircuit): Variational quantum circuit.
"""
num_qubits = len(data)
feature_map = EfficientSU2(num_qubits, reps=1)
qc = QuantumCircuit(num_qubits)
qc.compose(feature_map, inplace=True)
return qc
def variational_quantum_compression(data):
"""
Perform variational quantum compression on the given data.
Parameters:
data (array-like): Input data for compression.
Returns:
optimal_point (array-like): Optimal parameters found for the variational quantum circuit.
"""
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
def simulate_quantum_circuit_with_noise(qc):
"""
Simulate the quantum circuit with noise mitigation.
Parameters:
qc (QuantumCircuit): Quantum circuit to simulate.
Returns:
counts (dict): Resulting counts from the simulation.
"""
simulator = AerSimulator()
t_qc = transpile(qc, simulator)
job = execute(t_qc, backend=simulator, shots=1024)
result = job.result()
counts = result.get_counts()
return counts
def create_quantum_autoencoder(data):
"""
Create a quantum autoencoder for the given data.
Parameters:
data (array-like): Input data for the quantum autoencoder.
Returns:
encoder (QuantumCircuit): Encoder part of the quantum autoencoder.
decoder (QuantumCircuit): Decoder part of the quantum autoencoder.
"""
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
def quantum_autoencoder_compression(data):
"""
Perform quantum autoencoder compression on the given data.
Parameters:
data (array-like): Input data for compression.
Returns:
qc (QuantumCircuit): Quantum circuit representing the autoencoder.
"""
encoder, decoder = create_quantum_autoencoder(data)
qc = QuantumCircuit(len(data) * 2)
qc.compose(encoder, inplace=True)
qc.compose(decoder, inplace=True)
return qc
def visualize_quantum_results(counts):
"""
Visualize the results of the quantum circuit simulation.
Parameters:
counts (dict): Resulting counts from the simulation.
"""
plot_histogram(counts).show()