Mondragón Sampedro, FernandoÁlvarez-Estévez, Diego2026-09-172026-09-172025Mondragon-Sampedro, F., & Alvarez-Estevez, D. (2026). Quantum and Classical Kernels Applied to Classification of Unbalanced Datasets. In Proceedings XoveTIC 2025: Impulsando el talento científico (pp. 139-144). Servizo de Publicacións UDC. https://doi.org/10.17979/spu.23.c28978-84-9749-925-5https://hdl.handle.net/2183/49303Presentado en: VIII Congreso Xove TIC: impulsando el talento científico. Octubre, 2025, A Coruña.[Abstract] This work investigates quantum kernel methods for classification on unbalanced datasets. The study considers the effect of techniques commonly used in classical machine learning, such as kernel centering and cost-sensitive learning, when applied to quantum models. Experiments are conducted on five datasets, including MNIST-1D, the real-world dataset Glass6, and synthetic datasets tailored for quantum classifiers. Results indicate that quantum kernel methods can address unbalanced classification tasks albeit none of the techniques studied showed a statistically significant improvement of the scoring metrics. Results also highlight the influence of hyperparameterization and in particular, quantum bandwidth is identified as a critical hyperparameter for the performance of quantum models.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Quantum kernel methodsQuantum machine learningUnbalanced classificationHyperparameter optimizationconference outputopen access10.17979/spu.23.c28