Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

Harshil Singh Bhatia; Frank Phillipson

doi:10.1007/978-3-030-77980-1_7

Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

Harshil Singh Bhatia, Frank Phillipson^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data.

Original language	English
Title of host publication	Computational Science – ICCS 2021
Subtitle of host publication	21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI
Editors	Maciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Jack J. Dongarra
Publisher	Springer, Cham
Pages	84-97
ISBN (Electronic)	978-3-030-77980-1
ISBN (Print)	978-3-030-77979-5
DOIs	https://doi.org/10.1007/978-3-030-77980-1_7
Publication status	Published - 2021

Publication series

Series	Lecture Notes in Computer Science
Volume	12747
ISSN	0302-9743

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10.1007/978-3-030-77980-1_7

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Bhatia, H. S., & Phillipson, F. (2021). Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. In M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, P. M. A. Sloot, & J. J. Dongarra (Eds.), Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI (pp. 84-97). Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_7

Bhatia, Harshil Singh ; Phillipson, Frank. / Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI. editor / Maciej Paszynski ; Dieter Kranzlmüller ; Valeria V. Krzhizhanovskaya ; Peter M.A. Sloot ; Jack J. Dongarra. Springer, Cham, 2021. pp. 84-97 (Lecture Notes in Computer Science, Vol. 12747).

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title = "Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer",

abstract = "In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data.",

author = "Bhatia, {Harshil Singh} and Frank Phillipson",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

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doi = "10.1007/978-3-030-77980-1_7",

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Bhatia, HS & Phillipson, F 2021, Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. in M Paszynski, D Kranzlmüller, VV Krzhizhanovskaya, PMA Sloot & JJ Dongarra (eds), Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI. Springer, Cham, Lecture Notes in Computer Science, vol. 12747, pp. 84-97. https://doi.org/10.1007/978-3-030-77980-1_7

Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. / Bhatia, Harshil Singh; Phillipson, Frank.
Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI. ed. / Maciej Paszynski; Dieter Kranzlmüller; Valeria V. Krzhizhanovskaya; Peter M.A. Sloot; Jack J. Dongarra. Springer, Cham, 2021. p. 84-97 (Lecture Notes in Computer Science, Vol. 12747).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

N2 - In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data.

AB - In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data.

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Bhatia HS, Phillipson F. Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. In Paszynski M, Kranzlmüller D, Krzhizhanovskaya VV, Sloot PMA, Dongarra JJ, editors, Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI. Springer, Cham. 2021. p. 84-97. (Lecture Notes in Computer Science, Vol. 12747). doi: 10.1007/978-3-030-77980-1_7

Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

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