April 2, 2024, 7:42 p.m. | Javier Mancilla, Andr\'e Sequeira, Iraitz Montalb\'an, Tomas Tagliani, Frnacisco Llaneza, Claudio Beiza

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.00015v1 Announce Type: cross
Abstract: Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find better links between data features and the target class to be predicted even in such challenging scenarios and most importantly, enhanced generalization capabilities. In this work, we …

abstract arxiv credit cs.lg data datasets feature however interpretability machine machine learning q-fin.rm q-fin.st quant-ph quantum quantum kernels scoring spaces stage stat.ml struggle systems type vast

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