QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification

• URL: http://arxiv.org/abs/2604.11817v1
• Date: Fri, 10 Apr 2026 19:28:58 GMT
• Title: QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
• Abstract summary: We introduce QMC-Net, a hybrid architecture that processes six data channels using band-specific quantum circuits.<n>Results consistently outperform strong classical baselines and monolithic hybrid quantum models.
• Score: 19.28428432777636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 introduce QMC-Net, a hybrid architecture that processes six data channels using band-specific quantum circuits, enabling adaptive quantum feature encoding and transformation across channels. Experiments on the EuroSAT and SAT-6 datasets demonstrate that QMC-Net achieves accuracies of 93.80 % and 99.34 %, respectively, while a residual-enhanced variant further improves performance to 94.69 % and 99.39 %. These results consistently outperform strong classical baselines and monolithic hybrid quantum models, highlighting the effectiveness of data-aware quantum circuit design under NISQ constraints.

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