Quantum generative adversarial learning in photonics

TL;DR

This paper demonstrates the first experimental implementation of Quantum Generative Adversarial Networks (QGANs) in photonics using a silicon quantum photonic chip, showing high-quality data generation despite noise and device defects, thus supporting their feasibility on NISQ hardware.

Contribution

First experimental demonstration of QGANs in photonics on a programmable silicon quantum chip, analyzing robustness against noise and defects in near-term quantum devices.

Findings

QGANs achieved over 90% fidelity in data generation.

QGANs remained effective with up to 50% phase shifter damage.

QGANs tolerated phase noise up to 0.04π.

Abstract

Quantum Generative Adversarial Networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of Noisy Intermediate-Scale Quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time, and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90\%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04$\pi$. Our work sheds light on the feasibility of implementing QGANs on NISQ-era quantum hardware.