Simulating X-ray absorption spectroscopy of battery materials on a quantum computer

TL;DR

This paper explores quantum computing methods to simulate X-ray absorption spectra of battery materials, aiming to improve understanding of degradation mechanisms with fewer qubits and gates.

Contribution

It introduces three quantum algorithms for simulating X-ray absorption spectra, demonstrating their efficiency and applicability to complex battery-related systems.

Findings

Quantum algorithms can simulate spectra with fewer resources.

Monte-Carlo time-domain method is cost-effective for early fault-tolerant quantum computers.

Practical simulations of battery materials are feasible with current quantum technology.

Abstract

X-ray absorption spectroscopy is a crucial experimental technique for elucidating the mechanisms of structural degradation in battery materials. However, extracting information from the measured spectrum is challenging without high-quality simulations. In this work, we propose simulating near-edge X-ray absorption spectra as a promising application for quantum computing. It is attractive due to the ultralocal nature of X-ray absorption that significantly reduces the sizes of problems to be simulated, and because of the classical hardness of simulating spectra. We describe three quantum algorithms to compute the X-ray absorption spectrum and provide their asymptotic cost. One of these is a Monte-Carlo based time-domain algorithm, which is cost-friendly to early fault-tolerant quantum computers. We then apply the framework to an industrially relevant example, a CAS(22e,18o) active space for an O-Mn cluster in a Li-excess battery cathode, showing that practically useful simulations could be obtained with much fewer qubits and gates than ground-state energy estimation of the same material.