Quantum generative adversarial learning for simultaneous multiparameter estimation

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.