Impact of quantum noise on the training of quantum Generative Adversarial Networks
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.
Keywords
Cite
@article{arxiv.2203.01007,
title = {Impact of quantum noise on the training of quantum Generative Adversarial Networks},
author = {Kerstin Borras and Su Yeon Chang and Lena Funcke and Michele Grossi and Tobias Hartung and Karl Jansen and Dirk Kruecker and Stefan Kühn and Florian Rehm and Cenk Tüysüz and Sofia Vallecorsa},
journal= {arXiv preprint arXiv:2203.01007},
year = {2023}
}
Comments
6 pages, 5 figures, Proceedings of the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021)