Dynamical simulation via quantum machine learning with provable generalization

Gibbs, Joe; Holmes, Zoë; Caro, Matthias C.; Ezzell, Nicholas; Huang, Hsin-Yuan; Cincio, Lukasz; Sornborger, Andrew T.; Coles, Patrick J.

doi:10.1103/PhysRevResearch.6.013241

Title: Dynamical simulation via quantum machine learning with provable generalization

Journal Article · 04 March 2024 · Physical Review Research

DOI: https://doi.org/10.1103/PhysRevResearch.6.013241 · OSTI ID:2318757

; Holmes, Zoë;

; Ezzell, Nicholas;

; Cincio, Lukasz;

; Coles, Patrick J.

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. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota. Published by the American Physical Society 2024

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0020347

OSTI ID:: 2318757

Alternate ID(s):: OSTI ID: 2323523

Journal Information:: Physical Review Research, Journal Name: Physical Review Research Journal Issue: 1 Vol. 6; ISSN PPRHAI; ISSN 2643-1564

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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