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

doi:10.1088/1742-6596/2438/1/012093

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

Centre for Mathematical Biology

Research output: Contribution to journal › Conference article › peer-review

10 Citations (SciVal)

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.

Original language	English
Article number	012093
Journal	Journal of Physics: Conference Series
Volume	2438
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/2438/1/012093
Publication status	Published - 31 Dec 2023
Event	20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021 - Daejeon, Virtual, Korea, Republic of Duration: 29 Nov 2021 → 3 Dec 2021

Bibliographical note

Funding Information:
This project is supported by CERN’s Quantum Technology Initiative. L.F. is supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704, by the DOE QuantiSED Consortium under subcontract number 675352, by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/), and by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under grant contract numbers DE-SC0011090 and DE-SC0021006. S.K. acknowledges financial support from the Cyprus Research and Innovation Foundation under project “Future-proofing Scientific Applications for the Supercomputers of Tomorrow (FAST)”, contract no. COMPLEMENTARY/0916/0048.

ASJC Scopus subject areas

General Physics and Astronomy

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10.1088/1742-6596/2438/1/012093Licence: CC BY

Cite this

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title = "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.",

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N2 - 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.

AB - 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.

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

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