arXiv Machine LearningBy Jaiman Munshi (IonQ Team, App Dev Club, University of Maryland, College Park), Tanvi Tewary (IonQ Team, App Dev Club, University of Maryland, College Park), Sawyer Bloom (IonQ Team, App Dev Club, University of Maryland, College Park), Aidan Chu (IonQ Team, App Dev Club, University of Maryland, College Park), Chetan Maviti (IonQ Team, App Dev Club, University of Maryland, College Park), Kyon Winston-Bey (IonQ Team, App Dev Club, University of Maryland, College Park), Harshit Badjatia (IonQ Team, App Dev Club, University of Maryland, College Park), Farhan Kittur (IonQ Team, App Dev Club, University of Maryland, College Park), Vardhan Madhavarapu (IonQ Team, App Dev Club, University of Maryland, College Park), Varun Kota (IonQ Team, App Dev Club, University of Maryland, College Park), Joshua Kwon (IonQ Team, App Dev Club, University of Maryland, College Park), Nazia Rangwala-Vohra (IonQ Team, App Dev Club, University of Maryland, College Park), Franz Klein (IonQ Team, App Dev Club, University of Maryland, College Park)
QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery
arXiv:2607. 14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference.
Summary generated by The Flow from the publisher's feed. The full article
lives at arXiv Machine Learning.
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.
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.
arXiv:2607. 08928v1 Announce Type: cross Abstract: This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset.
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.