  We present an algorithm for learning a latent variable generative model via
generative adversarial learning where the canonical uniform noise input is
replaced by samples from a graphical model. This graphical model is learned by
a Boltzmann machine which learns low-dimensional feature representation of data
extracted by the discriminator. A quantum annealer, the D-Wave 2000Q, is used
to sample from this model. This algorithm joins a growing family of algorithms
that use a quantum annealing subroutine in deep learning, and provides a
framework to test the advantages of quantum-assisted learning in GANs. Fully
connected, symmetric bipartite and Chimera graph topologies are compared on a
reduced stochastically binarized MNIST dataset, for both classical and quantum
annealing sampling methods. The quantum-assisted associative adversarial
network successfully learns a generative model of the MNIST dataset for all
topologies, and is also applied to the LSUN dataset bedrooms class for the
Chimera topology. Evaluated using the Fr\'{e}chet inception distance and
inception score, the quantum and classical versions of the algorithm are found
to have equivalent performance for learning an implicit generative model of the
MNIST dataset.
