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

Kerstin Borras, Su Yeon Chang, Lena Funcke, Michele Grossi, Tobias Hartung, Karl Jansen, Dirk Kruecker, Stefan Kühn, Florian Rehm, Cenk Tüysüz, Sofia Vallecorsa

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number012093
JournalJournal of Physics: Conference Series
Issue number1
Publication statusAcceptance date - 29 Nov 2021
Event20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021 - Daejeon, Virtual, Korea, Republic of
Duration: 29 Nov 20213 Dec 2021

ASJC Scopus subject areas

  • Physics and Astronomy(all)


Dive into the research topics of 'Impact of quantum noise on the training of quantum Generative Adversarial Networks'. Together they form a unique fingerprint.

Cite this