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arXiv · 2110.07482

Large scale multi-node simulations of $\mathbb{Z}_2$ gauge theory quantum circuits using Google Cloud Platform

Erik Gustafson·Burt Holzman·James Kowalkowski·Henry Lamm·Andy C. Y. Li·Gabriel Perdue·Sergio Boixo·Sergei Isakov·Orion Martin·Ross Thomson·Catherine Vollgraff Heidweiller·Jackson Beall·Martin Ganahl·Guifre Vidal·Evan Peters
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Abstract

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units -- Application-specific Integrated Circuits (ASICs) specialized for Machine Learning -- may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating $\mathbb{Z}_2$ quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

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Erik Gustafson, Burt Holzman, James Kowalkowski, Henry Lamm, Andy C. Y. Li, Gabriel Perdue, Sergio Boixo, Sergei Isakov, Orion Martin, Ross Thomson, Catherine Vollgraff Heidweiller, Jackson Beall, Martin Ganahl, Guifre Vidal, Evan Peters. 2021-10-14. Large scale multi-node simulations of $\mathbb{Z}_2$ gauge theory quantum circuits using Google Cloud Platform. https://arxiv.org/abs/2110.07482

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