Learning quantum kernels with continuous-variable optical circuits

Kernel-based learning methods are central to classical machine learning, yet their quantum counterparts have largely remained limited to fixed circuit constructions. Most existing approaches aim to improve the performance of kernel-based support vector machines by heuristically tailoring kernel func...

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Autors principals:	Aikaterini Mandilara, Aristeides D Papadopoulos, Dimitris Syvridis
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2026-01-01
Col·lecció:	Machine Learning: Science and Technology
Matèries:	quantum kernel continuous-variable quantum optics kernel learning methods fisher criterion quasi-conformal transformations
Accés en línia:	https://doi.org/10.1088/2632-2153/ae7baf
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