Jha, Ravi KumarKasabov, NikolaBhattacharyya, SaugatCoyle, DamienPrasad, Girijesh2026-05-192026-05-192026-02-10Scientific Reports, ISSN: 2045-2322 (Print); 2045-2322 (Online), Nature Portfolio, 16(1), 8142-. doi: 10.1038/s41598-026-39392-92045-23222045-2322http://hdl.handle.net/10292/21114Quantum 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.Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.https://creativecommons.org/licenses/by/4.0/ClassificationEncoding functionFeature mapMachine learningQuantum kernelClassificationEncoding functionFeature mapMachine learningQuantum kernel46 Information and Computing Sciences4611 Machine LearningNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceJournal ArticleOpenAccess10.1038/s41598-026-39392-9