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.

使用可编程硅量子光子芯片，我们首次在光子学中实验性地演示了 QGAN 模型，并研究了噪声和缺陷对其性能的影响。我们的结果显示，即使在生成器的相移器的一半受损，或者生成器和鉴别器的所有相移器受到高达 0.04π 的相位噪声的条件下，QGAN 可以生成高质量的量子数据，保真度超过 90％。我们的工作为在 NISQ 时代的量子硬件上实施 QGAN 提供了可行性的线索。