Simulating Models of Challenging Correlated Molecules and Materials on the Sycamore Quantum Processor

Abstract

Simulating complex molecules and materials is an anticipated application of quantum devices. With the emergence of hardware designed to target strong quantum advantage in artificial tasks, we examine how the same hardware behaves in 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 α-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 classical recompilation and simulated data, we achieve quantitatively meaningful results deploying about one fifth of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over half 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.

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