Article
Version 1
Preserved in Portico This version is not peer-reviewed
On the Applicability of Quantum Machine Learning
Version 1
: Received: 10 May 2023 / Approved: 11 May 2023 / Online: 11 May 2023 (09:04:31 CEST)
Version 2 : Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (10:24:58 CEST)
Version 3 : Received: 7 June 2023 / Approved: 9 June 2023 / Online: 9 June 2023 (13:27:01 CEST)
Version 2 : Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (10:24:58 CEST)
Version 3 : Received: 7 June 2023 / Approved: 9 June 2023 / Online: 9 June 2023 (13:27:01 CEST)
A peer-reviewed article of this Preprint also exists.
Raubitzek, S.; Mallinger, K. On the Applicability of Quantum Machine Learning. Entropy 2023, 25, 992. https://doi.org/10.3390/e25070992 Raubitzek, S.; Mallinger, K. On the Applicability of Quantum Machine Learning. Entropy 2023, 25, 992. https://doi.org/10.3390/e25070992
Abstract
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the Variational Quantum Classifier (VQC) and the Quantum Kernel Estimator (QKE). We test the performance of these classifiers on six widely known and publicly available benchmark datasets and examine how their performance varies depending on the number of samples for artificially generated test classification data sets.
Keywords
quantum machine learning; Variational Quantum Circuit; Quantum Kernel Estimator; Qiskit; Ridge; Lasso; XGBoost; LightGBM; CatBoost; classification; quantum computing; boost classifiers; neural networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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