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.
|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|
|Publication status||Published - 2021|
|Series||Lecture Notes in Computer Science|