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Hyper-parameter Tuning for Quantum Support Vector MachineDEMIRTAS, F.   , TANYILDIZI, E. |
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Author keywords
grid computing, optimization, parameter estimation, quantum computing, support vector machines
References keywords
quantum(22), learning(16), machine(15), vector(10), support(10), optimization(8), neural(7), systems(6), kernel(6), search(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-11-30
Volume 22, Issue 4, Year 2022, On page(s): 47 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.04006
Web of Science Accession Number: 000920289700006
SCOPUS ID: 85150218461
Abstract
In recent years, the positive effect of quantum techniques on machine learning methods have been studied. Especially in training big data, quantum computing is beneficial in terms of speed. This study examined and applied the Quantum Support Vector Machine steps to the breast cancer dataset. Different types of feature maps used in the conversion of a classical dataset to a quantum dataset were examined using different dimensions. One of the factors that directly affect the performance of machine learning models is the correct selection of the hyper-parameters. These values must be obtained independent from the designer. Within the scope of the study, the hyper-parameter tuning methods, namely, Grid, Random, and Bayesian search methods, were examined. By using these methods, the hyper-parameters of the Support vector machine, which is one of the machine learning methods, were found. The performances of Linear, Non-linear and Quantum support vector machines were compared, and the running costs were analyzed. |
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| [1] C. Gong, Z. Dong, A. Gani, A. et al. "Quantum k-means algorithm based on a trusted server in quantum cloud computing," Quantum Inf Process 20, 130, 2021. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8] [2] E. Rieffel and W. Polak, "An introduction to quantum computing for non-physicists," ACM Comput. Surv. 32, 3 (Sept. 2000), 300-335. [CrossRef] [Web of Science Times Cited 141] [SCOPUS Times Cited 199] [3] R. Kohavi, and F. Provost, "Glossary of terms," Machine Learning -Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Machine Learning, 30, 271-274, 1998. [CrossRef] [4] B. E. Boser, I. M. Guyon, V. N. Vapnik, "A training algorithm for optimal margin classifiers," Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 5, pp. 144-152, 1992. [CrossRef] [5] E. Ozden, D. Guleryuz, "Optimized machine learning algorithms for investigating the relationship between economic development and human capital," Comput Econ (2021). [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 5] [6] J. Bergstra, Y. Bengio, "Random search for hyper-parameter optimization," Journal of Machine Learning Research, 13, 281-305, 2012. [7] H. A. Fayed, A. F. Atiya, "Speed up grid search for parameter selection of support vector machines," Applied Soft Computing, 80, 202-210, 2019. [CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 33] [8] R. G. Mantovani, A. L. D. Rossi, J. Vanschoren, B. Bischl and A. C. P. L. F. de Carvalho, "Effectiveness of random search in SVM hyper-parameter tuning," 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp.1-8. [CrossRef] [SCOPUS Times Cited 75] [9] R. Chatterjee, T. Yu, "Generalized coherent states, reproducing kernels, and quantum support vector machines," Quantum Inf. Commun. 17, 1292, 2017. [CrossRef] [10] T. Li, S. Chakrabarti, X. Wu, "Sublinear quantum algorithms for training linear and kernel-based classifiers," In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019), vol. PMLR 97, pp. 3815-3824, 2019. [CrossRef] [11] Y. Suzuki, H. Yano, Q. Gao, S. Uno, T. Tanaka, M. Akiyama, N. Yamamoto, "Analysis and synthesis of feature map for kernel-based quantum classifier," Quantum Machine Intelligence, 2(1), 1-9, 2020. [CrossRef] [12] Z. Luo, W. Zhang, Y. Li and M. Xiang, "SVM parameters tuning with quantum particles swarm optimization," 2008 IEEE Conference on Cybernetics and Intelligent Systems, 2008, pp. 324-329. [CrossRef] [SCOPUS Times Cited 7] [13] V. Tkachuk, "Quantum genetic algorithm based on qutrits and its application," Mathematical Problems in Engineering, vol. 2018, Article ID 8614073, 8 pages, 2018. [CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 17] [14] K. Mitarai, M. Negoro, M. Kitagawa, K. Fujii, "K. Quantum circuit learning," Phys. Rev. A 98, 032309, 2018. [CrossRef] [Web of Science Times Cited 476] [SCOPUS Times Cited 547] [15] O. Nairz, M. Arndt, A. Zeilinger, "Quantum interference experiments with large molecules," American Journal of Physics, 71, 319-325, April 2003. [CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 172] [16] B. E. Boser, I. M. Guyon, V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the fifth annual workshop on Computational learning theory. ACM, 1992, pp. 144-152. [CrossRef] [17] C. Cortes, V. N. Vladimir, "Support-vector networks," Machine Learning. 20 (3): 273-297, 1995. [CrossRef] [Web of Science Times Cited 31483] [18] V. Cherkassky, Y. Ma, "Practical selection of SVM parameters and noise estimation for SVM regression," Neural networks, 17(1), 113-126, 2004. [CrossRef] [Web of Science Times Cited 1412] [SCOPUS Times Cited 1739] [19] S. Huang N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu, "Applications of Support Vector Machine (SVM) learning in cancer genomics," Cancer Genomics Proteomics. 2018; 15(1):41-51. [CrossRef] [Web of Science Times Cited 572] [SCOPUS Times Cited 734] [20] S. S. Keerthi, C. J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural computation, 15(7), 1667-1689, 2003,. [CrossRef] [Web of Science Times Cited 1145] [SCOPUS Times Cited 1460] [21] A. Patle and D. S. Chouhan, "SVM kernel functions for classification," 2013 International Conference on Advances in Technology and Engineering (ICATE), 2013, pp. 1-9. [CrossRef] [SCOPUS Times Cited 135] [22] M. Y. Cho, T. T. Hoang, "A differential particle swarm optimization-based support vector machine classifier for fault diagnosis in power distribution systems," Advances in Electrical and Computer Engineering, vol.17, no.3, pp.51-60, 2017. [CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 7] [23] J. Suykens, J. Vandewalle, Least squares support vector machine classifiers," Neural Processing Letters 9, 293-300, 1999. [CrossRef] [Web of Science Times Cited 6853] [SCOPUS Times Cited 8679] [24] V. Cherkassky, Y. Ma, "Practical selection of SVM parameters and noise estimation for SVM regression," Neural networks, 17(1), pp. 113-126, 2004. [CrossRef] [Web of Science Times Cited 1412] [SCOPUS Times Cited 1739] [25] S. Boughorbel, J. Tarel and N. Boujemaa, "Conditionally positive definite kernels for SVM based image recognition," 2005 IEEE International Conference on Multimedia and Expo, 2005, pp. 113-116. [CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 54] [26] T. Hofmann, B. Scholkopf, A. J. Smola, "Kernel methods in machine learning," Ann. Statist. Volume 36, Number 3, 1171-1220, 2008. [CrossRef] [27] L. Yang, A. Shami, "On hyperparameter optimization of machine learning algorithms: Theory and practice," Neurocomputing, 415, 295-316, 2020. [CrossRef] [Web of Science Times Cited 584] [SCOPUS Times Cited 764] [28] Y. Bao, Z. Liu, "A fast Grid search method in support vector regression forecasting time series," In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning - IDEAL. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg, 2006. [CrossRef] [SCOPUS Times Cited 60] [29] A. C. Florea, A. C. R. Andonie, "Weighted random search for hyperparameter optimization," International Journal of Computers, Communications and Control, 14(2), 2019. [CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 25] [30] W. Ziyu, H. Frank, Z. Masrour, M. David, F. Nando, "Bayesian optimization in a billion dimensions via random embeddings," Journal of Artificial Intelligence Research. 55: 361-387, 2013. [CrossRef] [Web of Science Times Cited 175] [SCOPUS Times Cited 223] [31] J. Snoek, H. Larochelle, R. P. Adams, "Practical Bayesian optimization of machine learning algorithms," In Advances in neural information processing systems (pp. 2951-2959), 2012. [CrossRef] [32] J. Virmani, N. Dey, V. Kumar, "PCA-PNN and PCA-SVM based CAD systems for breast density classification. Applications of intelligent optimization in biology and medicine," Cham: Springer; p. 159-80, 2016. [CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 91] [33] V. Havlicek, A. D. Corcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, J. M. Gambetta, "Supervised learning with quantum-enhanced feature spaces," Nature, 567(7747), 209-212, 2019. [CrossRef] [Web of Science Times Cited 676] [SCOPUS Times Cited 805] [34] T. Goto, Q. H. Tran, K. Nakajima, "Universal approximation property of quantum feature map," arXiv:2009.00298, 2020. Retrieved March 14, 2021, [CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 24] [35] R. Wille, R. Van Meter and Y. Naveh, "IBM's Qiskit tool chain: Working with and developing for real quantum computers," 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019, pp. 1234-1240. [CrossRef] [SCOPUS Times Cited 63] [36] G. Uehara, S. Rao, M. Dobson, C. Tepedelenlioglu and A. Spanias, "Quantum neural network parameter estimation for photovoltaic fault detection," 12th International Conference on Information, Intelligence, Systems & Applications (IISA), 2021. pp. 1-7. [CrossRef] [SCOPUS Times Cited 10] [37] Basic Qiskit Syntax [online] Available: https://qiskit.org/textbook/chappendix/qiskit.html [38] C. Blank D. K. Park, J. K. K. Rhee, F. Petruccione, "Quantum classifier with tailored quantum kernel," npj Quantum Inf 6, 41, 2020. [CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 75] [39] Dataset.Available:https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic). [40] S. Vashisth, I. Dhall, G. Aggarwal, "Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis," Journal of Intelligent Systems,30(1), 998-1013, 2021. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6] [41] P. Rebentrost, M. Mohseni, S. Lloyd, "Quantum support vector machine for big data classification," Physical review letters Vol.113, Iss.13, 130503, 2014. [CrossRef] [Web of Science Times Cited 764] [SCOPUS Times Cited 945] Web of Science® Citations for all references: 46,070 TCR SCOPUS® Citations for all references: 18,701 TCR Web of Science® Average Citations per reference: 1,097 ACR SCOPUS® Average Citations per reference: 445 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2023-11-29 20:43 in 219 seconds. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
