QuCNet: Quantum Deep Learning Driven Multi-Circuit Network for Remote Sensing Image Classification

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, 85× 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 \textbf{scalable, hardware feasible quantum deep learning} for geospatial applications.