Experimental quantum natural gradient optimization in photonics

TL;DR

This paper demonstrates the first experimental implementation of quantum natural gradient optimization on a photonic chip, achieving chemical accuracy in molecular dissociation calculations and showcasing its advantages over traditional methods.

Contribution

First experimental realization of quantum natural gradient optimization in photonics, demonstrating improved convergence and accuracy in quantum chemistry applications.

Findings

Achieved chemical accuracy in He-H$^+$ dissociation curve

Verified the outperformance of QNG over other optimization methods

Pioneered QNG implementation on a programmable photonic chip

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.