quantum6g 1.3.0

This library is an automatic artificial intelligence library that combines Quantum and 6G technologies.

Quantum6G: Auto AI Advanced Quantum Neural Networks with 6G Technology

Quantum6G is an automatic artificial intelligence library that combines quantum computing and 6G technologies to build advanced quantum neural networks. It provides a high-level interface for constructing, training, and evaluating quantum neural networks. This library was developed by Quantum PIYA.

Installation

To install the Quantum6G library, simply run the following command:

pip install quantum6g

Getting Started

Here is a simple example to get started with the Quantum6G library:

from quantum6g import Quantum6G

Create a quantum neural network

quantum_6g = Quantum6G(output_unit=1, num_layers=4, epochs=2, loss='mse', input=4, batch_size=256, learning_rate=0.2)

Build the model

quantum_6g = quantum_6g.build_model(X_train, y_train, X_test, y_test)

Evaluate the model

print("Accuracy: {:.2f}%".format(quantum_6g[1][1] * 100))
print("Loss: {:.2f}%".format(quantum_6g[1][0] * 100))

Build and Fit Quantum6G_KNN --- from v1.2.5V

quantum_knn = Quantum6G_KNN(n_qubits=4, n_neighbors=6)
quantum_knn.fit(X_train, y_train)

Evaluate the Quantum6G_KNN model

quantum_pred = quantum_knn.predict(X_test,y_test)
quantum_accuracy = accuracy_score(y_test, quantum_pred)
print(f"Accuracy of Quantum6G_KNN: {quantum_accuracy:.3f}")

Build Quantum Model for QCNN (Quantum Convolutional Neural Network) --- from v1.3.0V

q_cnn = QCNN(
    input_shape=(2, 2, 1),
    output_neurons=10,
    loss_function="sparse_categorical_crossentropy",
    epochs=5,
    batch_size=32,
    optimizer="adam",
    n_layers=1,
    n_wires=4,
)
q_cnn_model = q_cnn.build_model()

Evaluate the QCNN model

q_cnn.benchmark(q_cnn_model, x_train_resized[..., np.newaxis], y_train, x_test_resized[..., np.newaxis], y_test)

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Documentation

For more information on how to use the Quantum6G library, please refer to the documentation available at [the soon].

Contributing

We welcome contributions to the Quantum6G library. If you would like to contribute, please fork the repository and make your changes, then submit a pull request.

License

The Quantum6G library is open source and released under the MIT license. For more information, please see the LICENSE file.