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.
By 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)
arXiv:2607. 11843v1 Announce Type: cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood.
By Junrui Zhang, Zemin Chen, Lusi Li, Mohammad Ghasemigol, Daniel Takabi, Rui Ning
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