TY - JOUR
T1 - Impact of quantum noise on the training of quantum Generative Adversarial Networks
AU - Borras, Kerstin
AU - Chang, Su Yeon
AU - Funcke, Lena
AU - Grossi, Michele
AU - Hartung, Tobias
AU - Jansen, Karl
AU - Kruecker, Dirk
AU - Kühn, Stefan
AU - Rehm, Florian
AU - Tüysüz, Cenk
AU - Vallecorsa, Sofia
N1 - 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.
PY - 2021/11/29
Y1 - 2021/11/29
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.
UR - http://www.scopus.com/inward/record.url?scp=85149770908&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2438/1/012093
DO - 10.1088/1742-6596/2438/1/012093
M3 - Conference article
AN - SCOPUS:85149770908
SN - 1742-6588
VL - 2438
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012093
T2 - 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021
Y2 - 29 November 2021 through 3 December 2021
ER -