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 proceedingConference article in proceedingAcademicpeer-review


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 languageEnglish
Title of host publicationComputational Science – ICCS 2021
Subtitle of host publication21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part VI
EditorsMaciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Jack J. Dongarra
PublisherSpringer, Cham
ISBN (Electronic)978-3-030-77980-1
ISBN (Print)978-3-030-77979-5
Publication statusPublished - 2021

Publication series

SeriesLecture Notes in Computer Science

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