Experimental evaluation of quantum machine learning algorithms

Monteiro Simoes, Ricardo Daniel; Huber, Patrick; Meier, Nicola; Smailov, Nikita; Füchslin, Rudolf M.; Stockinger, Kurt

doi:10.1109/ACCESS.2023.3236409

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26784

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Experimental evaluation of quantum machine learning algorithms
Authors:	Monteiro Simoes, Ricardo Daniel Huber, Patrick Meier, Nicola Smailov, Nikita Füchslin, Rudolf M. Stockinger, Kurt
et. al:	No
DOI:	10.1109/ACCESS.2023.3236409 10.21256/zhaw-26784
Published in:	IEEE Access
Volume(Issue):	11
Page(s):	6197
Pages to:	6208
Issue Date:	Jan-2023
Publisher / Ed. Institution:	IEEE
ISSN:	2169-3536
Language:	English
Subjects:	Quantum computing; Machine learning; Neural network
Subject (DDC):	006: Special computer methods
Abstract:	Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.
URI:	https://digitalcollection.zhaw.ch/handle/11475/26784
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT) Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:	Publikationen School of Engineering

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Monteiro Simoes, R. D., Huber, P., Meier, N., Smailov, N., Füchslin, R. M., & Stockinger, K. (2023). Experimental evaluation of quantum machine learning algorithms. IEEE Access, 11, 6197–6208. https://doi.org/10.1109/ACCESS.2023.3236409

Monteiro Simoes, R.D. et al. (2023) ‘Experimental evaluation of quantum machine learning algorithms’, IEEE Access, 11, pp. 6197–6208. Available at: https://doi.org/10.1109/ACCESS.2023.3236409.

R. D. Monteiro Simoes, P. Huber, N. Meier, N. Smailov, R. M. Füchslin, and K. Stockinger, “Experimental evaluation of quantum machine learning algorithms,” IEEE Access, vol. 11, pp. 6197–6208, Jan. 2023, doi: 10.1109/ACCESS.2023.3236409.

MONTEIRO SIMOES, Ricardo Daniel, Patrick HUBER, Nicola MEIER, Nikita SMAILOV, Rudolf M. FÜCHSLIN und Kurt STOCKINGER, 2023. Experimental evaluation of quantum machine learning algorithms. IEEE Access. Januar 2023. Bd. 11, S. 6197–6208. DOI 10.1109/ACCESS.2023.3236409

Monteiro Simoes, Ricardo Daniel, Patrick Huber, Nicola Meier, Nikita Smailov, Rudolf M. Füchslin, and Kurt Stockinger. 2023. “Experimental Evaluation of Quantum Machine Learning Algorithms.” IEEE Access 11 (January): 6197–6208. https://doi.org/10.1109/ACCESS.2023.3236409.

Monteiro Simoes, Ricardo Daniel, et al. “Experimental Evaluation of Quantum Machine Learning Algorithms.” IEEE Access, vol. 11, Jan. 2023, pp. 6197–208, https://doi.org/10.1109/ACCESS.2023.3236409.