Abstract
Quantum algorithms have become a popular research domain in recent times for discovering quantum-enhanced solutions in machine learning applications. Quantum kernels are one of the directions that establish such quantum-enhanced solutions to some extent. This work presents a detailed analysis of the quantum kernel approach leveraging feature maps and relevant hyperparameters to develop enhanced quantum kernels. The study includes a new high-order feature map and assesses five existing state-of-the-art feature maps for enhanced quantum kernel classifiers. Additionally, the significance of the rotational factor as a hyperparameter is highlighted for improving kernel performance. Also, it is analyzed whether different hyperparameter-tuned feature maps can lead to enhanced decision boundaries, demonstrating kernel expressivity. The analysis is undertaken on classification tasks using four different nonlinear datasets of distinct complexity. Comparative evaluations are also made with traditional machine learning models—Support Vector Machines (Linear and RBF), Naïve Bayes, Linear Discriminant Analysis, Decision Tree, Random Forest, Adaptive Boosting, and MLP. Overall, the study demonstrates that a well-tuned quantum feature map can enhance the generalization ability of quantum kernels, making them more effective for broader quantum-enhanced machine learning applications.
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The Breast Cancer Wisconsin (Diagnostic) data is available in Kaggle as well as can be obtained from the UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic)56.
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Acknowledgements
The authors acknowledge the partial support provided by the Ulster University Vice-Chancellor Research Scholarship for RJ. GP and SB acknowledge the partial support from the UKRI Strength in Places Project (81801): Smart Nano-Manufacturing Corridor. NK acknowledges the George Moor Professor Chair position (01.03.2020-01.03.2024).
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RJ developed the framework and data processing, designing and executing the experimental results. NK, GP, SB, and DC supervised the work with insights in results analysis. RJ prepared the original manuscript with the generation of figures and tables. All authors have reviewed and edited the manuscript.
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Jha, R.K., Kasabov, N., Bhattacharyya, S. et al. Comparative performance analysis of quantum feature maps for quantum kernel-based machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39392-9
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DOI: https://doi.org/10.1038/s41598-026-39392-9
