:py:mod:`kosmos.ml.models.vqc.encoding`
=======================================

.. py:module:: kosmos.ml.models.vqc.encoding







Classes
-------

.. py:class:: VQCEncoding(input_dim: int, output_dim: int)

   Bases: :py:class:`abc.ABC`

   Feature encoding for the VQC.

   Initialize the encoding.

   :param input_dim: The input dimension of the model.
   :type input_dim: int
   :param output_dim: The output dimension of the model.
   :type output_dim: int




   |

   .. rubric:: Properties

   .. py:property:: num_qubits

      Number of required qubits.



   |

   .. rubric:: Methods

   .. py:method:: apply_operation(features: torch.Tensor, wires: collections.abc.Sequence[int]) -> None

      Apply the PennyLane operation for encoding.

      :param features: Input features.
      :type features: Tensor
      :param wires: Target wires.
      :type wires: Sequence[int]




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.. py:class:: AngleEmbedding(input_dim: int, output_dim: int, rotation: Literal['X', 'Y', 'Z'])

   Bases: :py:class:`VQCEncoding`

   Angle embedding for the VQC.

   Initialize the angle embedding.

   :param input_dim: The input dimension of the model.
   :type input_dim: int
   :param output_dim: The output dimension of the model.
   :type output_dim: int
   :param rotation: The rotation to use for the angle embedding.
   :type rotation: Literal["X", "Y", "Z"]




   |

   .. rubric:: Properties

   .. py:property:: num_qubits

      Number of required qubits.



   |

   .. rubric:: Methods

   .. py:method:: apply_operation(features: torch.Tensor, wires: collections.abc.Sequence[int]) -> None

      Apply the angle embedding operation.

      :param features: Input features.
      :type features: Tensor
      :param wires: Target wires.
      :type wires: Sequence[int]




----

.. py:class:: AmplitudeEmbedding(input_dim: int, output_dim: int, pad_with: complex, *, normalize: bool)

   Bases: :py:class:`VQCEncoding`

   Amplitude embedding for the VQC.

   Initialize the amplitude embedding.

   :param input_dim: The input dimension of the model.
   :type input_dim: int
   :param output_dim: The output dimension of the model.
   :type output_dim: int
   :param pad_with: The input is padded with this constant to size :math:`2^n`.
   :type pad_with: complex
   :param normalize: Whether to normalize the features.
   :type normalize: bool




   |

   .. rubric:: Properties

   .. py:property:: num_qubits

      Number of required qubits.



   |

   .. rubric:: Methods

   .. py:method:: apply_operation(features: torch.Tensor, wires: collections.abc.Sequence[int]) -> None

      Apply the amplitude embedding operation.

      :param features: Input features.
      :type features: Tensor
      :param wires: Target wires.
      :type wires: Sequence[int]