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

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 symbolscript 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.

Automated summary

This study simulates the correlated electronic structure of complex molecules and materials using Google's Sycamore architecture quantum processor. By simplifying the electronic structure into suitable models and employing error mitigation and simulated data, the research team achieves meaningful results with a certain level of resource usage.