# Near-Term Quantum-Classical Associative Adversarial Networks

## TL;DR

This paper presents a hybrid quantum-classical adversarial network architecture that leverages a small quantum Boltzmann machine to improve learning quality on image datasets, suitable for near-term quantum devices.

## Contribution

Introduction of QAAN, a novel hybrid quantum-classical adversarial network architecture with a small quantum Boltzmann machine integrated into the discriminator.

## Key findings

- QAAN outperforms classical GANs on MNIST and CIFAR-10 datasets.
- QAAN achieves higher Inception scores and lower Fréchet Inception distances.
- Model is feasible for implementation on current quantum hardware.

## Abstract

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.