Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMonteiro Simoes, Ricardo Daniel-
dc.contributor.authorHuber, Patrick-
dc.contributor.authorMeier, Nicola-
dc.contributor.authorSmailov, Nikita-
dc.contributor.authorFüchslin, Rudolf M.-
dc.contributor.authorStockinger, Kurt-
dc.description.abstractMachine 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%.de_CH
dc.relation.ispartofIEEE Accessde_CH
dc.subjectQuantum computingde_CH
dc.subjectMachine learningde_CH
dc.subjectNeural networkde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleExperimental evaluation of quantum machine learning algorithmsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedIntelligent Information Systemsde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2023_MonteiroSimoes-etal_Experimental-evaluation-of-quantum-machine-learning-algorithms.pdf2.12 MBAdobe PDFThumbnail
Show simple item record
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.
Monteiro Simoes, R.D. et al. (2023) ‘Experimental evaluation of quantum machine learning algorithms’, IEEE Access, 11, pp. 6197–6208. Available at:
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.
Monteiro Simoes, Ricardo Daniel, et al. “Experimental Evaluation of Quantum Machine Learning Algorithms.” IEEE Access, vol. 11, Jan. 2023, pp. 6197–208,

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.