Article
Version 2
Preserved in Portico This version is not peer-reviewed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 evaluate the performance of these classifiers on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples in artificially generated test classification datasets. Our results demonstrate that the VQC and QKE exhibit superior performance compared to basic machine learning algorithms such as advanced linear regression models (Ridge and Lasso). However, they do not match the accuracy and runtime performance of sophisticated modern boosting classifiers like XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field.\\ To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.
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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