Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection

Forward-looking sonar object detection is essential for underwater perception, yet deployment on embedded platforms requires highly compact models. To address this challenge, we explore quantum computing and introduce Quantum-Gated LiteSSD, a parameter-efficient hybrid quantum--classical detector that reformulates QuCNet-style multi-circuit quantum processing as an identity-centered channel-gating mechanism for spatial feature modulation. Experiments on the Marine Debris Watertank dataset and UATD forward-looking sonar benchmarks demonstrate an effective parameter--accuracy trade-off. The proposed detector achieves 90.84% mAP50 on Watertank with approximately 62× fewer parameters than YOLO26s and 164.3× fewer parameters than SSD-VGG16. On UATD, the model achieves 70.37% mAP50 with only 0.150M parameters, making it approximately 4.1× smaller than SSGA-YOLO while retaining meaningful multi-class detection capability.