Applications and experimental realizations of quantum generative adversarial networks

Quantum generative adversarial networks (qGANs) represent a potentially powerful quantum machine learning tool for the analysis of quantum data and quantum processes. This talk presents a review of the theory of quantum generative adversarial networks, describes their application to pattern recognition and to quantum state and process tomography, and summarizes the current experimental state of the art for implementing qGANs. We introduce a novel quantum generative network model based on the recently proposed quantum Wasserstein-1 distance.