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  4/2022 - 6

Hyper-parameter Tuning for Quantum Support Vector Machine

DEMIRTAS, F. See more information about DEMIRTAS, F. on SCOPUS See more information about DEMIRTAS, F. on IEEExplore See more information about DEMIRTAS, F. on Web of Science, TANYILDIZI, E. See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on Web of Science
 
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Download PDF pdficon (1,442 KB) | Citation | Downloads: 175 | Views: 426

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

Abstract
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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.


References | Cited By  «-- Click to see who has cited this paper

[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 3] [SCOPUS Times Cited 3]


[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 130] [SCOPUS Times Cited 182]


[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 1] [SCOPUS Record]


[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 5] [SCOPUS Times Cited 8]


[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 57]


[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 6]


[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 8] [SCOPUS Times Cited 13]


[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 330] [SCOPUS Times Cited 360]


[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 138] [SCOPUS Times Cited 166]


[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]


[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 1338] [SCOPUS Times Cited 1634]


[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 408] [SCOPUS Times Cited 503]


[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 1111] [SCOPUS Times Cited 1419]


[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 88]


[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 3] [SCOPUS Times Cited 4]


[23] J. Suykens, J. Vandewalle, Least squares support vector machine classifiers," Neural Processing Letters 9, 293-300, 1999.
[CrossRef] [Web of Science Times Cited 6463] [SCOPUS Times Cited 8179]


[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 1338] [SCOPUS Times Cited 1634]


[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 31] [SCOPUS Times Cited 51]


[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 259] [SCOPUS Times Cited 331]


[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 53]


[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 10] [SCOPUS Times Cited 14]


[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 141] [SCOPUS Times Cited 163]


[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 41] [SCOPUS Times Cited 84]


[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 479] [SCOPUS Times Cited 538]


[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 6] [SCOPUS Times Cited 6]


[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 46]


[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 7]


[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 44] [SCOPUS Times Cited 46]


[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 1] [SCOPUS Times Cited 2]


[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 587] [SCOPUS Times Cited 713]




References Weight

Web of Science® Citations for all references: 12,875 TCR
SCOPUS® Citations for all references: 16,310 TCR

Web of Science® Average Citations per reference: 307 ACR
SCOPUS® Average Citations per reference: 388 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 2023-01-25 09:08 in 255 seconds.




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