Impact of quantum noise on the training of quantum Generative Adversarial Networks

TL;DR

This paper investigates how quantum noise affects the training of quantum GANs, analyzing error thresholds, hyperparameter importance, and mitigation strategies using classical simulations of noisy quantum devices.

Contribution

It provides the first systematic study of quantum noise impacts on qGAN training, including error thresholds and mitigation effects in a high-energy physics context.

Findings

Training remains reliable below certain error thresholds.

Readout error mitigation improves training outcomes.

Hyperparameter tuning is crucial under noisy conditions.

Abstract

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM's Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.