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 requirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.
@article{arxiv.2204.10269,
title = {Dynamical simulation via quantum machine learning with provable generalization},
author = {Joe Gibbs and Zoë Holmes and Matthias C. Caro and Nicholas Ezzell and Hsin-Yuan Huang and Lukasz Cincio and Andrew T. Sornborger and Patrick J. Coles},
journal= {arXiv preprint arXiv:2204.10269},
year = {2024}
}