Near-term quantum algorithm for computing molecular and materials properties based on recursive variational series methods

TL;DR

This paper introduces a recursive variational series quantum algorithm that uses Chebyshev polynomial expansion to estimate molecular properties on near-term quantum computers, demonstrated through Green's and autocorrelation functions.

Contribution

It presents a novel recursive variational series method leveraging Chebyshev polynomial expansion for practical quantum property estimation on near-term devices.

Findings

Successfully computed one-particle Green's function

Evaluated autocorrelation function in the time domain

Demonstrated feasibility on near-term quantum hardware

Abstract

Determining the properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is: how might we use imperfect near-term quantum computers to solve problems of practical value? We propose a quantum algorithm to estimate the properties of molecules using near-term quantum devices. The method is a recursive variational series estimation method, where we expand an operator of interest in terms of Chebyshev polynomials and evaluate each term in the expansion using a variational quantum algorithm. We test our method by computing the one-particle Green's function in the energy domain and the autocorrelation function in the time domain.