Simulating challenging correlated molecules and materials on the Sycamore quantum processor

TL;DR

This paper demonstrates the simulation of complex correlated molecules and materials on Google's Sycamore quantum processor, achieving meaningful results with reduced gate resources and error mitigation, bridging artificial quantum advantage and real physical problems.

Contribution

It introduces a method to simulate correlated electronic structures on a superconducting quantum processor, translating artificial quantum advantage into physically relevant molecular and material modeling.

Findings

Achieved meaningful simulation results with about 1/5 of the gates used in artificial advantage experiments.

Enhanced simulation efficiency by choosing models tailored to hardware capabilities.

Successfully simulated complex molecules and materials, demonstrating quantum advantage in physical problem settings.

Abstract

Simulating complex molecules and materials is an anticipated application of quantum devices. With strong quantum advantage demonstrated in artificial tasks, we examine how such advantage translates into modeling physical problems of correlated electronic structure. We simulate static and dynamical electronic structure on a superconducting quantum processor derived from Google's Sycamore architecture for two representative correlated electron problems: the nitrogenase iron-sulfur molecular clusters, and $\alpha$-ruthenium trichloride, a proximate spin-liquid material. To do so, we simplify the electronic structure into low-energy spin models that fit on the device. With extensive error mitigation and assistance from classically simulated data, we achieve quantitatively meaningful results deploying about 1/5 of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over 1/2 of the gate resources when choosing a model that suits the hardware. Our work serves to convert artificial measures of quantum advantage into a physically relevant setting.