用量子增强视觉变换器，提升遥感图像洪水检测精度。
Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery
- 结合量子电路与视觉变换器，分路处理遥感图像特征。
- 准确率从84.48%升至94.47%，F1值达0.944。
- 适合需要高精度洪水监测的灾害管理场景。
可靠的洪水检测对灾害管理至关重要，但传统深度学习模型在处理遥感数据中的高维非线性复杂性时表现不佳。为此，我们提出一种新型量子增强视觉变换器（Quantum-Enhanced Vision Transformer, ViT），融合了变换器的全局感知能力与量子计算的特征表达优势。基于遥感影像，构建混合架构：输入通过经典ViT主干和4量子比特参数化量子电路组成的量子分支并行处理，分别提取局部与全局特征，再进行融合以优化二分类性能。实验结果表明，该混合模型显著优于经典ViT基线，整体准确率由84.48%提升至94.47%，F1分数从0.841增至0.944。尤其在复杂地形下，量子模块显著增强了两类样本的判别能力。研究验证了量子-经典混合模型在水文监测与地球观测中的潜力。
原文摘要 · Abstract (English)
Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.
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