Quantum-enhanced quantum Monte Carlo: an industrial view

TL;DR

This paper evaluates a quantum-enhanced method to improve classical quantum Monte Carlo calculations for chemistry and material science, demonstrating potential for more accurate industrially relevant simulations.

Contribution

It introduces a quantum-assisted approach to enhance AFQMC calculations, testing its effectiveness on chemical and material science models with insights into trial wave function impacts.

Findings

Trial wave functions beyond single Slater determinants improve energy accuracy.

Quantum enhancement yields energies close to chemical accuracy in molecular systems.

Larger fidelities do not always translate to better AFQMC results in material models.

Abstract

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representatives of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which are industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow to generate energies close to chemical accuracy compared to full configuration interaction (FCI) or experimental results. As a representative for material science we study a quasi-1D Fermi-Hubbard model derived from CuBr2, a compound displaying electronic structure properties analogous to cuprates. We find that trial wave functions with both, significantly larger fidelities and lower energies over a Hartree-Fock solution, do not necessarily lead to better AFQMC results.