根据遥感图像通道特性定制量子电路，提升分类准确率
QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
- 用通道统计特征控制量子电路参数，实现数据自适应编码
- 在EuroSAT和SAT-6上分别达93.80%和99.34%准确率
- 适合需要高效处理多光谱遥感数据的研究者
混合量子-经典模型为复杂数据学习提供了新路径，但其在多波段遥感图像上的应用常依赖通用、与数据无关的量子电路，无法捕捉波段间统计差异。本文提出一种数据驱动框架，将信道级统计量如香农熵、方差、谱平坦度和边缘密度映射到定制化量子电路的超参数。基于此，我们设计QMC-Net，采用针对各波段的专用量子电路处理六通道数据，实现跨通道自适应量子特征编码与变换。在EuroSAT和SAT-6数据集上的实验表明，QMC-Net分别达到93.80%和99.34%的准确率，残差增强版本进一步提升至94.69%和99.39%。结果持续优于强基准模型及统一量子架构，验证了在近似量子计算（NISQ）约束下，数据感知量子电路设计的有效性。
原文摘要 · Abstract (English)
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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