QuCNet：量子深度学习驱动的多线路网络用于遥感图像分类
QuCNet: Quantum Deep Learning Driven Multi-Circuit Network for Remote Sensing Image Classification
把轻量卷积编码器与十六路并行四量子比特可训练量子线路组合，在仅 87K 参数下完成遥感场景分类，并给出可表达性分析与 IBM 真实量子硬件推理验证，为极低参数量、硬件可行的量子-经典混合视觉骨干提供可复用设计范式。
原文依据 QuCNet integrates a lightweight convolutional encoder with sixteen parallel four-qubit trainable quantum circuits (TQCs)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 QuCNet，一种用于高效遥感图像分类的混合量子-经典网络。QuCNet 将轻量卷积编码器与十六个并行的四量子比特可训练量子线路（TQC）相结合，并在混合循环权重共享（HCWS）策略下训练。该设计在参数量极低（87K，比已有混合模型小 85 倍）的前提下提升了可表达性。在可表达性分析的指导下，所提出的量子配置在近中期量子设备上保持稳定梯度并缓解贫瘠高原问题。在七个遥感基准（AID、AIDER、UC Merced、NWPU-45、EuroSAT、IIITDMJ Smoke 与 USTC SmokeRS）上的大量实验表明，QuCNet 相较经典 CNN 基线持续提升精度与泛化能力。此外，仅在 IBM 量子处理器（ibm_torino、ibm_fez）上进行的硬件推理证实了其在真实噪声与连通性约束下的鲁棒性。这些结果表明了一条面向地理空间应用的可扩展、硬件可行的量子深度学习实用路径。
英文原文摘要 1094 字符
We present QuCNet, a hybrid quantum classical network for efficient remote sensing image classification. QuCNet integrates a lightweight convolutional encoder with sixteen parallel four-qubit trainable quantum circuits (TQCs) trained under a Hybrid Cyclic Weight-Sharing (HCWS) strategy. This design enhances expressibility while keeping the parameter count extremely low 87K, 85x smaller than prior hybrid models). Guided by expressibility analysis, the proposed quantum configuration maintains stable gradients and mitigates barren plateaus on near term quantum devices. Extensive experiments across seven remote sensing benchmarks (AID, AIDER, UC Merced, NWPU-45, EuroSAT, IIITDMJ Smoke, and USTC SmokeRS) demonstrate that QuCNet consistently improves accuracy and generalization over classical CNN baselines. Furthermore, hardware only inference on IBM Quantum processors (ibm_torino, ibm_fez) confirms robustness under realistic noise and connectivity constraints. These results suggest a practical path toward scalable, hardware feasible quantum deep learning for geospatial applications.