Source code for strawberryfields.apps.qchem.utils

# Copyright 2020 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
This module contains various utility functions needed to perform quantum chemistry calculations with
Strawberry Fields.
"""
from typing import Tuple

import numpy as np
from scipy.constants import c, h, m_u, pi
from thewalrus import quantum


[docs]def duschinsky( Li: np.ndarray, Lf: np.ndarray, ri: np.ndarray, rf: np.ndarray, wf: np.ndarray, m: np.ndarray ) -> Tuple[np.ndarray, np.ndarray]: r"""Generate the Duschinsky rotation matrix :math:`U` and displacement vector :math:`\delta`. The Duschinsky transformation relates the normal coordinates of the initial and final states in a vibronic transition, :math:`q_i` and :math:`q_f` respectively, as: .. math:: q_f = U q_i + d, where :math:`U` is the Duschinsky rotation matrix and :math:`d` is a vector giving the displacement between the equilibrium structures of the two states involved in the vibronic transition. The normal coordinates of a molecule can be represented in terms of atomic displacements as: .. math:: q = L^T \sqrt{m} (r -r_e), where :math:`r_e` represents the equilibrium geometry of the molecule, :math:`m` represents atomic masses and :math:`L` is a matrix containing the eigenvectors of the mass-weighted Hessian. The Duschinsky parameters :math:`U` and :math:`d` can be obtained as: .. math:: U = L_f^T L_i, .. math:: d = L_f^T \sqrt{m} (r_e^i-r_e^f). Note that :math:`i` and :math:`f` refer to the initial and final states, respectively. The parameter :math:`d` is usually represented as a dimensionless parameter :math:`\delta` as: .. math:: \delta = l^{-1} d, where :math:`l` is a diagonal matrix containing the vibrational frequencies :math:`\omega` of the final state: .. math:: l_{kk} = \left ( \frac{\hbar }{2 \pi \omega_k c} \right )^{1/2}, where :math:`\hbar` is the reduced Planck constant and :math:`c` is the speed of light. The vibrational normal mode matrix for a molecule with :math:`M` vibrational modes and :math:`N` atoms is a :math:`3N \times M` matrix where :math:`M = 3N - 6` for nonlinear molecules and :math:`M = 3N - 5` for linear molecules. The Duschinsky rotation matrix of a molecule is an :math:`M \times M` matrix and the Duschinsky displacement vector has :math:`M` components. **Example usage:** >>> Li = np.array([[-0.28933191], [0.0], [0.0], [0.95711104], [0.0], [0.0]]) >>> Lf = np.array([[-0.28933191], [0.0], [0.0], [0.95711104], [0.0], [0.0]]) >>> ri = np.array([-0.0236, 0.0, 0.0, 1.2236, 0.0, 0.0]) >>> rf = np.array([0.0, 0.0, 0.0, 1.4397, 0.0, 0.0]) >>> wf = np.array([1363.2]) >>> m = np.array([11.0093] * 3 + [1.0078] * 3) >>> U, delta = duschinsky(Li, Lf, ri, rf, wf, m) >>> U, delta (array([[0.99977449]]), array([-1.17623073])) Args: Li (array): mass-weighted normal modes of the initial electronic state Lf (array): mass-weighted normal modes of the final electronic state ri (array): equilibrium molecular geometry of the initial electronic state rf (array): equilibrium molecular geometry of the final electronic state wf (array): normal mode frequencies of the final electronic state in units of :math:`\mbox{cm}^{-1}` m (array): atomic masses in unified atomic mass units Returns: tuple[array, array]: Duschinsky rotation matrix :math:`U`, Duschinsky displacement vector :math:`\delta` """ U = Lf.T @ Li d = Lf.T * m ** 0.5 @ (ri - rf) l0_inv = np.diag((h / (wf * 100.0 * c)) ** (-0.5) * 2.0 * pi) / 1.0e10 * m_u ** 0.5 delta = np.array(d @ l0_inv) return U, delta
[docs]def read_gamess(file) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: r"""Reads molecular data from the output file generated by the GAMESS quantum chemistry package :cite:`schmidt1993general`. This function extracts the atomic coordinates (r), atomic masses (m), vibrational frequencies (w), and normal modes (l) of a molecule from the output file of a vibrational frequency calculation performed with the GAMESS quantum chemistry package. The output file must contain the results of a `RUNTYP=HESSIAN` calculation performed with GAMESS. We recommend checking the output of this function with the GAMESS results to assure that the GAMESS output file is parsed correctly. **Example usage:** >>> r, m, w, l = read_gamess('../BH_data.out') >>> r # atomic coordinates array([[0.0000000, 0.0000000, 0.0000000], [1.2536039, 0.0000000, 0.0000000]]) >>> m # atomic masses array([11.00931, 1.00782]) >>> w # vibrational frequencies array([19.74, 19.73, 0.00, 0.00, 0.00, 2320.32]) >>> l # normal modes array([[-0.0000000e+00, -7.5322000e-04, -8.7276210e-02, 0.0000000e+00, 8.2280900e-03, 9.5339055e-01], [-0.0000000e+00, -8.7276210e-02, 7.5322000e-04, 0.0000000e+00, 9.5339055e-01, -8.2280900e-03], [ 2.8846925e-01, -2.0000000e-08, 2.0000000e-08, 2.8846925e-01, -2.0000000e-08, 2.0000000e-08], [ 2.0000000e-08, 2.8846925e-01, -2.0000000e-08, 2.0000000e-08, 2.8846925e-01, -2.0000000e-08], [-2.0000000e-08, 2.0000000e-08, 2.8846925e-01, -2.0000000e-08, 2.0000000e-08, 2.8846925e-01], [-8.7279460e-02, 0.0000000e+00, 0.0000000e+00, 9.5342606e-01, -0.0000000e+00, -0.0000000e+00]]) Args: file (str): path to the GAMESS output file Returns: tuple[array, array, array, array]: atomic coordinates, atomic masses, normal mode frequencies, normal modes """ with open(file, "r") as f: r = [] m = [] w = [] l = [] for line in f: if "INPUT CARD> $data" in line or "INPUT CARD> $DATA" in line: line = [next(f) for _ in range(3)][-1] while "end" not in line and "END" not in line: r.append(np.array(line.rstrip().split()[-3:], float)) line = next(f).rstrip() if "ATOMIC WEIGHTS" in line: next(f) for _ in range(len(r)): m.append(np.array(next(f).rstrip().split()[-1:], float)) if "FREQUENCY" in line: line = line.rstrip().split() n_mode = len(line) - 1 w.append(np.array(line[-n_mode:], float)) while f.readline() != "\n": pass d = [] for _ in range(len(r) * 3): d.append(f.readline().rstrip().split()[-n_mode:]) l.append(np.array(d, float).T) if not r: raise ValueError("No atomic coordinates found in the output file") if not m: raise ValueError("No atomic masses found in the output file") if not w: raise ValueError("No vibrational frequencies found in the output file") return ( np.concatenate(r).reshape(len(r), 3), np.concatenate(m), np.concatenate(w), np.concatenate(l), )
def prob(samples: list, excited_state: list) -> float: r"""Estimate probability of observing an excited state. The probability is estimated by calculating the relative frequency of the excited state among the samples. **Example usage:** >>> excited_state = [0, 2] >>> samples = [[0, 2], [1, 1], [0, 2], [2, 0], [1, 1], [0, 2], [1, 1], [1, 1], [1, 1]] >>> prob(samples, excited_state) 0.3333333333333333 Args: samples list[list[int]]: a list of samples excited_state (list): a Fock state Returns: float: probability of observing a Fock state in the given samples """ if len(samples) == 0: raise ValueError("The samples list must not be empty") if len(excited_state) == 0: raise ValueError("The excited state list must not be empty") if not len(excited_state) == len(samples[0]): raise ValueError("The number of modes in the samples and the excited state must be equal") if np.any(np.array(excited_state) < 0): raise ValueError("The excited state must not contain negative values") return samples.count(excited_state) / len(samples) def marginals(mu: np.ndarray, V: np.ndarray, n_max: int, hbar: float = 2.0) -> np.ndarray: r"""Generate single-mode marginal distributions from the displacement vector and covariance matrix of a Gaussian state. **Example usage:** >>> mu = np.array([0.00000000, 2.82842712, 0.00000000, ... 0.00000000, 0.00000000, 0.00000000]) >>> V = np.array([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], ... [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], ... [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], ... [0.0, 0.0, 0.0, 1.0, 0.0, 0.0], ... [0.0, 0.0, 0.0, 0.0, 1.0, 0.0], ... [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]) >>> n_max = 10 >>> marginals(mu, V, n_max) array([[1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [1.35335284e-01, 2.70670567e-01, 2.70670566e-01, 1.80447044e-01, 9.02235216e-02, 3.60894085e-02, 1.20298028e-02, 3.43708650e-03, 8.59271622e-04, 1.90949249e-04], [1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]) Args: mu (array): displacement vector V (array): covariance matrix n_max (int): maximum number of vibrational quanta in the distribution hbar (float): the value of :math:`\hbar` in the commutation relation :math:`[\x,\p]=i\hbar`. Returns: array[list[float]]: marginal distributions """ if not V.shape[0] == V.shape[1]: raise ValueError("The covariance matrix must be a square matrix") if not len(mu) == len(V): raise ValueError( "The dimension of the displacement vector and the covariance matrix must be equal" ) if n_max <= 0: raise ValueError("The number of vibrational states must be larger than zero") n_modes = len(mu) // 2 p = np.zeros((n_modes, n_max)) for mode in range(n_modes): mui, vi = quantum.reduced_gaussian(mu, V, mode) for i in range(n_max): p[mode, i] = np.real(quantum.density_matrix_element(mui, vi, [i], [i], hbar=hbar)) return p