Examples
========


-------
Creating a QNN circuit
-------

.. code-block:: python

    from qailab.circuit import build_circuit, RotationalEncoder, CXEntangler, RealAmplitudesBlock

    input_encoder = RotationalEncoder('x','input')
    quantum_circuit = build_circuit(
        3,
        [
            input_encoder,
            CXEntangler(),
            RealAmplitudesBlock('weight'),
            input_encoder, #You can put the same block twice in different parts of the circuit. It will encode the same parameters
            CXEntangler(),
            RealAmplitudesBlock('weight')
        ],
        measure_qubits=[0,1]
        )



-------
Making a hybrid neural network
-------

.. code-block:: python

    import torch.nn as nn
    from qailab.torch import QLayer

    qlayer = QLayer(
        quantum_circuit,
        shots = 2048
        )

    sequential_net = nn.Sequential(
        nn.Linear(4,qlayer.in_features),
        nn.ReLU()
        qlayer,
        nn.Linear(qlayer.out_features,4),
        nn.Softmax()
    )


-------
Training a QModel instance
-------

.. code-block:: python

    model = QModel(
        module=sequential_net,
        loss=nn.CrossEntropyLoss(),
        optimizer_type='adam',
        learning_rate=0.001,
        batch_size=4,
        validation_fraction=0.1,
        epochs=10
        )

    model.fit(X,y)