qiskit_addon_qcbm.born_machine
==============================

.. py:module:: qiskit_addon_qcbm.born_machine

.. autoapi-nested-parse::

   Define the Quantum Circuit Born Machine.



Classes
-------

.. autoapisummary::

   qiskit_addon_qcbm.born_machine.QCBM
   qiskit_addon_qcbm.born_machine.MMD


Module Contents
---------------

.. py:class:: QCBM(num_qubits, num_layers, ansatz=None, data=None)

   Quantum Circuit Born Machine


   .. py:attribute:: num_layers


   .. py:attribute:: num_qubits


   .. py:attribute:: data
      :value: None



   .. py:method:: draw()

      Draw the QCBM circuit.



   .. py:method:: print()

      Provide the stats on the QCBM.



   .. py:method:: train(data=None, loss_fcn=None, num_iterations=10, num_shots=100, backend=FakeFez())

      Train the QCBM.



   .. py:method:: compute_loss(params, ansatz_isa, sampler, loss_fcn, train_history, target_probs)

      Return the value of the loss function.

      :param params: Array of ansatz parameters
      :type params: ndarray
      :param ansatz_isa: Parameterized transpiled ansatz
      :type ansatz_isa: QuantumCircuit
      :param sampler: Sampler primitive instance
      :type sampler: SamplerV2
      :param loss_fcn: Function using to compute the loss
      :type loss_fcn: python function
      :param train_history: Dictionary for storing intermediate results
      :type train_history: dict
      :param target_probs: the actual probability distribution
      :type target_probs: ndarray

      :returns: Scalar loss value
      :rtype: float



   .. py:method:: plot_compare_model_and_target_probs(x_max, target_probs, qcbm_probs)

      Compare the probabilities obtained with QCBM with the
      actual probability distribution.



.. py:class:: MMD(rbf_sigmas, nparange_hilbert_dim)

   Maximum Mean Discrepancy


   .. py:attribute:: K


   .. py:attribute:: rbf_sigmas


   .. py:method:: k_expval(px, py)


   .. py:method:: mmd_loss(px, py)