Experimental quantum natural gradient optimization in photonics

• URL: http://arxiv.org/abs/2310.07371v1
• Date: Wed, 11 Oct 2023 10:41:51 GMT
• Title: Experimental quantum natural gradient optimization in photonics
• Authors: Yizhi Wang, Shichuan Xue, Yaxuan Wang, Jiangfang Ding, Weixu Shi, Dongyang Wang, Yong Liu, Yingwen Liu, Xiang Fu, Guangyao Huang, Anqi Huang, Mingtang Deng, and Junjie Wu
• Abstract summary: Variational quantum algorithms (VQAs) promise practical quantum applications in the Noisy Intermediate-Scale Quantum era. The quantum natural gradient (QNG) can achieve faster convergence and avoid local minima more easily. We utilize a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time.
• Score: 11.72584828456107
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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