# Near-Term Quantum-Classical Associative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.13205 · HTML: https://mlsanity.com/paper/1905.13205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 3 of 3)

- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Training Quantum Boltzmann Machines with Coresets](https://mlsanity.com/paper/2307.14459/) (2023)
- [Machine Learning for Quantum Computing Specialists](https://mlsanity.com/paper/2404.18555/) (2024)

## Abstract (arXiv)

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'echet 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.