  Generative adversarial learning is one of the most exciting recent
breakthroughs in machine learning---a subfield of artificial intelligence that
is currently driving a revolution in many aspects of modern society. It has
shown splendid performance in a variety of challenging tasks such as image and
video generations. More recently, a quantum version of generative adversarial
learning has been theoretically proposed and shown to possess the potential of
exhibiting an exponential advantage over its classical counterpart. Here, we
report the first proof-of-principle experimental demonstration of quantum
generative adversarial learning in a superconducting quantum circuit. We
demonstrate that, after several rounds of adversarial learning, a quantum state
generator can be trained to replicate the statistics of the quantum data output
from a digital qubit channel simulator, with a high fidelity ($98.8\%$ on
average) that the discriminator cannot distinguish between the true and the
generated data. Our results pave the way for experimentally exploring the
intriguing long-sought-after quantum advantages in machine learning tasks with
noisy intermediate-scale quantum devices.
