arXiv Machine Learning
Jun 26

Tailor Made Embeddings for Quantum Machine Learning

arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.

By Aldo Lamarre, Dominik \v{S}afr\'anek
arXiv Machine Learning
Jul 27

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.

By Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis
arXiv AI
Jun 16

Quantum Machine Learning for Industrial Applications

arXiv:2606. 14822v1 Announce Type: cross Abstract: Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures.

By L\'eo Monbroussou