Dynamical simulation via quantum machine learning with provable generalization

ORAL

Abstract

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data re- quirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.

*ZH acknowledges support from the LANL Mark Kac Fellowship. MCC was sup- ported by the TopMath Graduate Center of the TUM Graduate School at the Technical University of Mu- nich, Germany, the TopMath Program at the Elite Net- work of Bavaria, by a doctoral scholarship of the Ger- man Academic Scholarship Foundation (Studienstiftung des deutschen Volkes), and by the BMWi (PlanQK).NE was supported by the U.S. DOE, Department of En- ergy Computational Science Graduate Fellowship under Award Number DE-SC0020347. HH is supported by a Google PhD Fellowship. PJC and ATS acknowledge ini- tial support from the Los Alamos National Laboratory (LANL) ASC Beyond Moore's Law project. ATS was also supported by the Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory under project number 20210116DR. LC ac- knowledges support from LDRD program of LANL under project number 20200022DR. LC and PJC were also sup- ported by the U.S. DOE, Office of Science, Office of Ad- vanced Scientific Computing Research, under the Quan- tum Computing Application Teams (QCAT) program.

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Publication: Dynamical simulation via quantum machine learning with provable generalization arXiv:2204.10269

Presenters

  • Joe Gibbs

    • AWE
    • Atomic Weapons Establishment

Authors

  • Joe Gibbs

    • AWE
    • Atomic Weapons Establishment