Near-Term Quantum-Classical Associative Adversarial Networks
This work addresses the challenge of enhancing machine learning performance for image generation tasks using near-term quantum devices, though it appears incremental as it builds on existing classical GANs with a quantum component.
The authors tackled the problem of improving generative adversarial networks by integrating a small quantum Boltzmann machine, achieving higher learning quality on MNIST and CIFAR-10 datasets as measured by Inception score and Fréchet Inception distance.
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 Fréchet 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.