On the Applicability of Quantum Machine Learning

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 7; p. 992
• Main Authors: Raubitzek, Sebastian, Mallinger, Kevin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.06.2023; MDPI
• Subjects: Algorithms; Analysis; Architecture; Artificial intelligence; Circuits; Classification; Classifiers; Data mining; Data structures; Datasets; Empirical analysis; Experiments; Feature maps; Integrated circuits; Lasso; Libraries; Lie groups; Machine learning; Neural networks; Performance evaluation; Phase transitions; Qiskit; Quantum computing; quantum kernel estimator; quantum machine learning; Quantum mechanics; Quantum physics; Regression models; Ridge; Semiconductor chips; Simulators; variational quantum circuit; variational quantum circuit; Lasso; LightGBM; neural networks; classification; Qiskit; quantum machine learning; XGBoost; quantum computing; quantum kernel estimator; boost classifiers; CatBoost; Ridge
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25070992

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 circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as 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. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. 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.