arXiv:2607. 21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.
By Guillermo Rubi\~nos Rodr\'iguez, Mart\'in Ottavianelli, Mateo Alonso, Gonzalo Bl\'azquez Gil, Boris-Stephan Rauchmann, Pablo D\'iez-Valle, Sergio Altares-L\'opez
arXiv:2606. 28252v1 Announce Type: cross Abstract: Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings.
By Akshay Bhagwan Sonawane, Sophie Choe, Lakshman Tamil
arXiv:2608. 04379v1 Announce Type: cross Abstract: We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy.
By Minseo Seong, Youngwook Kim
arXiv:2607. 09113v1 Announce Type: cross Abstract: Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias.
By Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong, Rajchawit Sarochawikasit
We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy. Unlike prior approaches that employ orthogonal decomposition as preprocessing, we intentionally introduce correlated features that are more physically compatible with QNN.
arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.
By Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada, Martin Roetteler, Iordanis Kerenidis