AI RESEARCH
QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
arXiv CS.CV
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ArXi:2604.11817v1 Announce Type: cross Hybrid quantum-classical models offer a promising route for learning from complex data; however, their application to multi-band remote sensing imagery often relies on generic, data-agnostic quantum circuits that fail to account for channel-specific statistical variability. In this work, we propose a data-driven framework that maps band-level statistics such as Shannon Entropy, Variance, Spectral Flatness, and Edge Density to the hyperparameters of customized quantum circuits. Building on this framework, we.