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.

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

pip install quantum6g

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

from quantum6g import Quantum6G

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

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

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

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

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}")

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()

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

You can donate for this project!

ETH - ERC20: 0xa6F7170Ca63cf284A8ba6339b565445468E04Ff2

BTC - Bech32: bc1qfek2lun4tc7d7zftz0v4auxc9dzn77h9xq9x26v02u6s3rgl7hesxt4r2h

USDT - TRC20: TWVcF24DjPnGfhegmJjBQw2iE4vxQBuYTY

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

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.

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