Quantum generative adversarial learning in photonics
This work addresses the feasibility of QGANs for quantum computing applications in the NISQ era, showing robustness to hardware imperfections, but it is incremental as it builds on existing QGAN concepts with experimental validation.
The researchers tackled the challenge of implementing Quantum Generative Adversarial Networks (QGANs) on noisy near-term quantum devices by experimentally demonstrating a QGAN model on a programmable silicon quantum photonic chip, achieving high-quality quantum data generation with over 90% fidelity even under significant noise and defects.
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$π$. Our work sheds light on the feasibility of implementing QGANs on NISQ-era quantum hardware.