Quantum generative adversarial learning in photonics
Yizhi Wang Shichuan Xue Yaxuan Wang Yong Liu Jiangfang Ding Weixu Shi Dongyang Wang Yingwen Liu Xiang Fu Guangyao Huang Anqi Huang Mingtang Deng Junjie Wu
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.