  Generative adversarial learning is currently one of the most prolific fields
in artificial intelligence due to its great performance in a variety of
challenging tasks such as photorealistic image and video generation. While a
quantum version of generative adversarial learning has emerged that promises
exponential advantages over its classical counterpart, its experimental
implementation and potential applications with accessible quantum technologies
remain explored little. Here, we report an experimental demonstration of
quantum generative adversarial learning with the assistance of adaptive
feedback that is based on stochastic gradient descent algorithm. Its
performance is explored by applying this technique to the adaptive
characterization of quantum dynamics and simultaneous estimation of multiple
phases. These results indicate the intriguing advantages of quantum generative
adversarial learning even in the presence of deleterious noise, and pave the
way towards quantum-enhanced information processing applications.
