Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry

By Harshdeep Singh et al

Published on June 2, 2023

Read the original document by opening this link in a new tab.

Table of Contents

I. INTRODUCTION
II. CHEMISTRY ON A QUANTUM COMPUTER
III. OPTIMIZERS
IV. METHODOLOGY

Summary

Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms for applications in quantum chemistry. In this work, a comparative performance analysis of classical optimizers is presented based on different quantum chemistry applications. The study evaluates the performance of popular optimizers in ideal and noisy quantum circuit conditions using simulations of various molecules. The variational quantum algorithms rely on classical optimizers to train parameterized quantum circuits for solving ground-state properties of molecules. The different optimizers are classified into gradient-based, gradient-free, and quantum-hardware-specific methods. The performance assessment includes evaluation of ground state energy error, dissociation energy error, and dipole moment error. The study provides insights into the efficiency of optimizers in variational quantum algorithms for quantum chemistry applications.