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

Quantum Physics [Submitted on 10 Apr 2026] QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification Md Aminur Hossain, Ayush V. Patel, Biplab Banerjee 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. Comments: Accepted in ICPR 2026, 15 pages Subjects: Quantum Physics (quant-ph); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.11817 [quant-ph]   (or arXiv:2604.11817v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.11817 Focus to learn more Journal reference: ICPR 2026 Submission history From: Md Aminur Hossain [view email] [v1] Fri, 10 Apr 2026 19:28:58 UTC (3,487 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CV References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)