  We introduce a new hybrid quantum-classical adversarial machine learning
architecture called a quantum-classical associative adversarial network (QAAN).
This architecture consists of a classical generative adversarial network with a
small auxiliary quantum Boltzmann machine that is simultaneously trained on an
intermediate layer of the discriminator of the generative network. We
numerically study the performance of QAANs compared to their classical
counterparts on the MNIST and CIFAR-10 data sets, and show that QAANs attain a
higher quality of learning when evaluated using the Inception score and the
Fr\'{e}chet Inception distance. As the QAAN architecture only relies on
sampling simple local observables of a small quantum Boltzmann machine, this
model is particularly amenable for implementation on the current and next
generations of quantum devices.
