Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions

TL;DR

This paper demonstrates that hybrid quantum generative adversarial networks (HQGANs) can learn continuous distributions effectively even with noise, and validates their performance through simulations and experiments on a real quantum processor.

Contribution

It introduces a noise-robust hybrid quantum GAN architecture, analyzes its performance under noise, and provides experimental validation on a quantum device.

Findings

HQGANs maintain performance despite added noise

Training parameters influence computational efficiency

Successful training demonstrated on Rigetti's quantum hardware

Abstract

The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical and experimental explorations will most likely be required to understand its power. There has been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modelling. In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise. We devise a simple way of adding different types of noise to the quantum generator circuit, and numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions, and show that the performance of HQGANs remain unaffected. We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. We then perform the training on Rigetti's Aspen-4-2Q-A quantum processing unit, and present the results from the training. Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.