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Modules
davidson module: This module is to find lowest eigenvalues with Davidson algorithm.
erpa module: Code to generate the eigenvalue problem for the ERPA equations
givens_rotations module: Givens rotations routines.
linear_qubit_operator module: LinearQubitOperator is a linear operator from QubitOperator.
rdm_reconstruction module
sparse_tools module: This module provides functions to interface with scipy.sparse.
wave_fitting module: Functions for fitting simple oscillating functions
wedge_product module: This module contains methods to lift tensors to higher spaces through the Grassmann wedge product.
Classes
class Davidson: Davidson algorithm to get the n states with smallest eigenvalues.
class DavidsonError: Exceptions.
class DavidsonOptions: Davidson algorithm iteration options.
class LinearQubitOperator: A LinearOperator implied from a QubitOperator.
class LinearQubitOperatorOptions: Options for LinearQubitOperator.
class ParallelLinearQubitOperator: A LinearOperator from a QubitOperator with multiple processors.
class QubitDavidson: Davidson algorithm applied to a QubitOperator.
class SparseDavidson: Davidson algorithm for a sparse matrix.
Functions
append_random_vectors(...): Appends exactly col orthonormal random vectors for vectors.
boson_ladder_sparse(...): Make a matrix representation of a singular bosonic ladder operator in the Fock space.
boson_operator_sparse(...): Initialize a Scipy sparse matrix in the Fock space from a bosonic operator.
double_givens_rotate(...): Apply a double Givens rotation.
eigenspectrum(...): Compute the eigenspectrum of an operator.
erpa_eom_hamiltonian(...): Evaluate sum{a,b,c,d}h{a, b, d, c}
expectation(...): Compute the expectation value of an operator with a state.
expectation_computational_basis_state(...): Compute expectation value of operator with a state.
expectation_db_operator_with_pw_basis_state(...): Compute expectation value of a dual basis operator with a plane wave computational basis state.
expectation_one_body_db_operator_computational_basis_state(...): Compute expectation value of a 1-body dual-basis operator with a plane wave computational basis state.
expectation_three_body_db_operator_computational_basis_state(...): Compute expectation value of a 3-body dual-basis operator with a plane wave computational basis state.
expectation_two_body_db_operator_computational_basis_state(...): Compute expectation value of a 2-body dual-basis operator with a plane wave computational basis state.
fermionic_gaussian_decomposition(...): Decompose a matrix into a sequence of Givens rotations and particle-hole transformations on the last fermionic mode.
fit_known_frequencies(...): Fits a set of known exponential components to a dataset
generate_linear_qubit_operator(...): Generates a LinearOperator from a QubitOperator.
generate_parity_permutations(...): Generates the permutations and sign of a sequence by constructing a tree where the nth level contains all n-permutations of m (n < m) objects.
generate_random_vectors(...): Generates orthonormal random vectors with col columns.
get_gap(...): Compute gap between lowest eigenvalue and first excited state.
get_ground_state(...): Compute lowest eigenvalue and eigenstate.
get_linear_qubit_operator_diagonal(...): Return a linear operator's diagonal elements.
get_number_preserving_sparse_operator(...): Initialize a Scipy sparse matrix in a specific symmetry sector.
get_sparse_operator(...): Map an operator to a sparse matrix.
givens_decomposition(...): Decompose a matrix into a sequence of Givens rotations.
givens_decomposition_square(...): Decompose a square matrix into a sequence of Givens rotations.
givens_matrix_elements(...): Compute the matrix elements of the Givens rotation that zeroes out one of two row entries.
givens_rotate(...): Apply a Givens rotation to coordinates i and j of an operator.
inner_product(...): Compute inner product of two states.
jordan_wigner_ladder_sparse(...): Make a matrix representation of a fermion ladder operator.
jordan_wigner_sparse(...): Initialize a Scipy sparse matrix from a FermionOperator.
jw_configuration_state(...): Function to produce a basis state in the occupation number basis.
jw_get_ground_state_at_particle_number(...): Compute ground energy and state at a specified particle number.
jw_hartree_fock_state(...): Function to produce Hartree-Fock state in JW representation.
jw_number_indices(...): Return the indices for n_electrons in n_qubits under JW encoding
jw_number_restrict_operator(...): Restrict a Jordan-Wigner encoded operator to a given particle number
jw_number_restrict_state(...): Restrict a Jordan-Wigner encoded state to a given particle number
jw_sparse_givens_rotation(...): Return the matrix (acting on a full wavefunction) that performs a Givens rotation of modes i and j in the Jordan-Wigner encoding.
jw_sparse_particle_hole_transformation_last_mode(...): Return the matrix (acting on a full wavefunction) that performs a particle-hole transformation on the last mode in the Jordan-Wigner encoding.
jw_sz_indices(...): Return the indices of basis vectors with fixed Sz under JW encoding.
jw_sz_restrict_operator(...): Restrict a Jordan-Wigner encoded operator to a given Sz value
jw_sz_restrict_state(...): Restrict a Jordan-Wigner encoded state to a given Sz value
kronecker_operators(...): Return the Kronecker product of multiple sparse.csc_matrix operators.
orthonormalize(...): Orthonormalize vectors, so that they're all normalized and orthogoal.
prony(...): Estimates amplitudes and phases of a sparse signal using Prony's method.
qubit_operator_sparse(...): Initialize a Scipy sparse matrix from a QubitOperator.
single_quad_op_sparse(...): Make a matrix representation of a singular quadrature operator in the Fock space.
singlet_erpa(...): Generate the singlet ERPA equations
sparse_eigenspectrum(...): Perform a dense diagonalization.
swap_columns(...): Swap columns i and j of matrix M.
swap_rows(...): Swap rows i and j of matrix M.
valdemoro_reconstruction(...): Build a 3-RDM by cumulant expansion and setting 3rd cumulant to zero
variance(...): Compute variance of operator with a state.
wedge(...): Implement the wedge product between left_tensor and right_tensor
wrapped_kronecker(...): Return the Kronecker product of two sparse.csc_matrix operators.