peropq.variational_unitary#

Module Contents#

Classes#

VariationalUnitary

class representing the variational unitary ansataz.
class peropq.variational_unitary.VariationalUnitary(hamiltonian, number_of_layer, time)#

class representing the variational unitary ansataz.

Parameters:
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).

Parameters:

new_array (numpy.typing.NDArray) –

Return type:

None

get_initial_trotter_vector()#

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

Return type:

numpy.typing.NDArray

flatten_theta(theta)#

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

Parameters:

theta (numpy.typing.NDArray) –

Return type:

numpy.typing.NDArray

set_theta_to_trotter()#

Sets the variational parameters to the Trotter parameters.

Return type:

None

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.

Parameters:

  • left_indices (numpy.typing.NDArray) –

  • right_indices (numpy.typing.NDArray) –

Return type:

numpy.typing.NDArray

calculate_traces()#

Calculate the trace tensor.

Return type:

None

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.

Parameters:

theta (numpy.typing.ArrayLike) –

Return type:

float