  Quantum machine learning is expected to be one of the first potential
general-purpose applications of near-term quantum devices. A major recent
breakthrough in classical machine learning is the notion of generative
adversarial training, where the gradients of a discriminator model are used to
train a separate generative model. In this work and a companion paper, we
extend adversarial training to the quantum domain and show how to construct
generative adversarial networks using quantum circuits. Furthermore, we also
show how to compute gradients -- a key element in generative adversarial
network training -- using another quantum circuit. We give an example of a
simple practical circuit ansatz to parametrize quantum machine learning models
and perform a simple numerical experiment to demonstrate that quantum
generative adversarial networks can be trained successfully.
