Experimental quantum natural gradient optimization in photonics
Abstract
Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the Noisy Intermediate-Scale Quantum era. The performance of VQAs heavily depends on the optimization method. Compared with gradient-free and ordinary gradient descent methods, the quantum natural gradient (QNG), which mirrors the geometric structure of the parameter space, can achieve faster convergence and avoid local minima more easily, thereby reducing the cost of circuit executions. We utilized a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time. We obtained the dissociation curve of the He-H cation and achieved chemical accuracy, verifying the outperformance of QNG optimization on a photonic device. Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.
Cite
@article{arxiv.2310.07371,
title = {Experimental quantum natural gradient optimization in photonics},
author = {Yizhi Wang and Shichuan Xue and Yaxuan Wang and Jiangfang Ding and Weixu Shi and Dongyang Wang and Yong Liu and Yingwen Liu and Xiang Fu and Guangyao Huang and Anqi Huang and Mingtang Deng and Junjie Wu},
journal= {arXiv preprint arXiv:2310.07371},
year = {2023}
}