  Generative adversarial networks are an emerging technique with wide
applications in machine learning, which have achieved dramatic success in a
number of challenging tasks including image and video generation. When equipped
with quantum processors, their quantum counterparts--called quantum generative
adversarial networks (QGANs)--may even exhibit exponential advantages in
certain machine learning applications. Here, we report an experimental
implementation of a QGAN using a programmable superconducting processor, in
which both the generator and the discriminator are parameterized via layers of
single- and multi-qubit quantum gates. The programmed QGAN runs automatically
several rounds of adversarial learning with quantum gradients to achieve a Nash
equilibrium point, where the generator can replicate data samples that mimic
the ones from the training set. Our implementation is promising to scale up to
noisy intermediate-scale quantum devices, thus paving the way for experimental
explorations of quantum advantages in practical applications with near-term
quantum technologies.
