Using Variational Eigensolvers on Low-End Hardware to Find the Ground
State Energy of Simple Molecules
- URL: http://arxiv.org/abs/2310.19104v1
- Date: Sun, 29 Oct 2023 18:36:18 GMT
- Title: Using Variational Eigensolvers on Low-End Hardware to Find the Ground
State Energy of Simple Molecules
- Authors: T. Powers, R.M. Rajapakse
- Abstract summary: Key properties of physical systems can be described by the eigenvalues of matrices that represent the system.
Computational algorithms that determine the eigenvalues of these matrices exist, but they generally suffer from a loss of performance as the matrix grows in size.
This process can be expanded to quantum computation to find the eigenvalues with better performance than the classical algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Key properties of physical systems can be described by the eigenvalues of
matrices that represent the system. Computational algorithms that determine the
eigenvalues of these matrices exist, but they generally suffer from a loss of
performance as the matrix grows in size. This process can be expanded to
quantum computation to find the eigenvalues with better performance than the
classical algorithms. One application of such an eigenvalue solver is to
determine energy levels of a molecule given a matrix representation of its
Hamiltonian using the variational principle. Using a variational quantum
eigensolver, we determine the ground state energies of different molecules. We
focus on the choice of optimization strategy for a Qiskit simulator on low-end
hardware. The benefits of several different optimizers were weighed in terms of
accuracy in comparison to an analytic classical solution as well as code
efficiency.
Related papers
- Laplace transform based quantum eigenvalue transformation via linear combination of Hamiltonian simulation [13.96848357202551]
We propose an efficient quantum algorithm for performing a class of eigenvalue transformations that can be expressed as a certain type of matrix Laplace transformation.
We demonstrate that our eigenvalue transformation approach can solve this problem without explicitly inverting $A$.
arXiv Detail & Related papers (2024-11-06T15:47:48Z) - Efficient conversion from fermionic Gaussian states to matrix product states [48.225436651971805]
We propose a highly efficient algorithm that converts fermionic Gaussian states to matrix product states.
It can be formulated for finite-size systems without translation invariance, but becomes particularly appealing when applied to infinite systems.
The potential of our method is demonstrated by numerical calculations in two chiral spin liquids.
arXiv Detail & Related papers (2024-08-02T10:15:26Z) - Tensor networks based quantum optimization algorithm [0.0]
In optimization, one of the well-known classical algorithms is power iterations.
We propose a quantum realiziation to circumvent this pitfall.
Our methodology becomes instance agnostic and thus allows one to address black-box optimization within the framework of quantum computing.
arXiv Detail & Related papers (2024-04-23T13:49:11Z) - Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction [55.57072563835959]
spectral graph neural networks are characterized by filters.
We propose an eigenvalue correction strategy that can free filters from the constraints of repeated eigenvalue inputs.
arXiv Detail & Related papers (2024-01-28T08:12:00Z) - Quantum eigenvalue processing [0.0]
Problems in linear algebra can be solved on a quantum computer by processing eigenvalues of the non-normal input matrices.
We present a Quantum EigenValue Transformation (QEVT) framework for applying arbitrary transformations on eigenvalues of block-encoded non-normal operators.
We also present a Quantum EigenValue Estimation (QEVE) algorithm for operators with real spectra.
arXiv Detail & Related papers (2024-01-11T19:49:31Z) - Vectorization of the density matrix and quantum simulation of the von
Neumann equation of time-dependent Hamiltonians [65.268245109828]
We develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations.
We show that one of these linearizations of the von-Neumann equation corresponds to the standard case in which the state vector becomes the column stacked elements of the density matrix.
A quantum algorithm to simulate the dynamics of the density matrix is proposed.
arXiv Detail & Related papers (2023-06-14T23:08:51Z) - Quantum algorithms for matrix operations and linear systems of equations [65.62256987706128]
We propose quantum algorithms for matrix operations using the "Sender-Receiver" model.
These quantum protocols can be used as subroutines in other quantum schemes.
arXiv Detail & Related papers (2022-02-10T08:12:20Z) - Contour Integral-based Quantum Algorithm for Estimating Matrix
Eigenvalue Density [5.962184741057505]
We propose a quantum algorithm for computing the eigenvalue density in a given interval.
The eigenvalue count in a given interval is derived as the probability of observing a bit pattern in a fraction of the qubits of the output state.
arXiv Detail & Related papers (2021-12-10T08:58:44Z) - Variational Adiabatic Gauge Transformation on real quantum hardware for
effective low-energy Hamiltonians and accurate diagonalization [68.8204255655161]
We introduce the Variational Adiabatic Gauge Transformation (VAGT)
VAGT is a non-perturbative hybrid quantum algorithm that can use nowadays quantum computers to learn the variational parameters of the unitary circuit.
The accuracy of VAGT is tested trough numerical simulations, as well as simulations on Rigetti and IonQ quantum computers.
arXiv Detail & Related papers (2021-11-16T20:50:08Z) - Quantum algorithms for spectral sums [50.045011844765185]
We propose new quantum algorithms for estimating spectral sums of positive semi-definite (PSD) matrices.
We show how the algorithms and techniques used in this work can be applied to three problems in spectral graph theory.
arXiv Detail & Related papers (2020-11-12T16:29:45Z) - Approximate Graph Spectral Decomposition with the Variational Quantum
Eigensolver [1.0152838128195465]
Spectral graph theory studies the relationships between the eigenvectors and eigenvalues of Laplacian and adjacency matrices and their associated graphs.
The Variational Quantum Eigensolver (VQE) algorithm was proposed as a hybrid quantum/classical algorithm.
In this paper, we will expand upon the VQE algorithm to analyze the spectra of directed and undirected graphs.
arXiv Detail & Related papers (2019-12-27T23:27:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.