Dynamical simulation via quantum machine learning with provable generalization

doi:10.1103/PhysRevResearch.6.013241

Dynamical simulation via quantum machine learning with provable generalization

2024-03-05

15

OA

AI

DOI：10.1103/PhysRevResearch.6.013241

Original

Original request for help

Save

Abstract

En 中文

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.

Dynamical simulation via quantum machine learning with provable generalization

Abstract

Journal

Organization