peropq.variational_unitary
==========================

.. py:module:: peropq.variational_unitary


Classes
-------

.. autoapisummary::

   peropq.variational_unitary.VariationalUnitary


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

.. py:class:: VariationalUnitary(hamiltonian, number_of_layer, time)

   class representing the variational unitary ansatz.


   .. py:attribute:: n_terms
      :type:  int


   .. py:attribute:: pauli_string_list
      :type:  collections.abc.Sequence[peropq.pauli_bitstring.PauliString]


   .. py:attribute:: depth
      :type:  int


   .. py:attribute:: theta
      :type:  numpy.typing.NDArray


   .. py:attribute:: cjs
      :type:  collections.abc.Sequence[complex]


   .. py:attribute:: time
      :type:  float


   .. py:attribute:: test
      :type:  numpy.typing.NDArray


   .. py:attribute:: trace_calculated
      :value: False



   .. py:method:: update_theta(new_array)

       Update theta ensuring that the condition Sum_i theta_i dt_i= is ensured.

      :param new_array: the new array containing the variational parameters. It's shape must be (R - 1, n_terms).



   .. py:method:: get_initial_trotter_vector()

      Get the variational parameters corresponding to the Trotterization. Useful to initialize the optimization.



   .. py:method:: flatten_theta(theta)

      Returns the variational parameters as flatten (R-1)*n_terms array. Useful to pass to a minimization function.



   .. py:method:: unflatten_theta(flat_theta)

      Returns the flattened variational parameters as an array with shape (depth,n_terms).



   .. py:method:: set_theta_to_trotter()

      Sets the variational parameters to the Trotter parameters.



   .. py:method:: chi_tensor(left_indices, right_indices)

      Vectorized function to calculate all the chi coefficient at once.

      :param left_indices: indices of the left tensor which give non-zero contributions in the calculation of chi.
      :param right_indices: indices of the right tensor which give non-zero contributions in the calculation of chi.
      :returns: numpy array representing the tensor used in the norm calculation.



   .. py:method:: calculate_traces()

      Calculate the trace tensor.



   .. py:method:: c2_squared(theta = ())

      Perturbative 2-norm.

      :param theta: parameters of the variational unitary.
      :returns: the perturbative approximation of the 2-norm difference between the exact and the variational representation.