García Veiga, XiánSanjurjo Amado, José RodrigoAmor, Margarita2026-09-172026-09-172025García Veiga, X., Sanjurjo Amado, J. R., & Amor, M. (2026). Real-Time Line Detection via GPU-Based Hough Transform. In Proceedings XoveTIC 2025: Impulsando el talento científico (pp. 145-152). Servizo de Publicacións UDC. https://doi.org/10.17979/spu.23.c29978-84-9749-925-5https://hdl.handle.net/2183/49302Presentado 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 methodsUnbalanced classificationCost-sensitive learningQuantum machine learningHyperparameter optimizationconference outputopen access10.17979/spu.23.c29