  We introduce a fully quantum generative adversarial network intended for use
with binary data. The architecture incorporates several features found in other
classical and quantum machine learning models, which up to this point had not
been used in conjunction. In particular, we incorporate noise reuploading in
the generator, auxiliary qubits in the discriminator to enhance expressivity,
and a direct connection between the generator and discriminator circuits,
obviating the need to access the generator's probability distribution. We show
that, as separate components, the generator and discriminator perform as
desired. We empirically demonstrate the expressive power of our model on both
synthetic data as well as low energy states of an Ising model. Our
demonstrations suggest that the model is not only capable of reproducing
discrete training data, but also of potentially generalizing from it.
