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

arXiv: https://arxiv.org/abs/2609.14025 · HTML: https://mlsanity.com/paper/2609.14025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Barren Plateaus in Variational Quantum Computing](https://mlsanity.com/paper/2405.00781/) (2024)
- [Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models](https://mlsanity.com/paper/2606.03748/) (2026)
- [Real-time Convolutional Neural Networks for Emotion and Gender Classification](https://mlsanity.com/paper/1710.07557/) (2017)
- [Quantum-soft QUBO Suppression for Accurate Object Detection](https://mlsanity.com/paper/2007.13992/) (2020)
- [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](https://mlsanity.com/paper/2212.00352/) (2022)
- [Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation](https://mlsanity.com/paper/2311.12912/) (2023)
- [Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications](https://mlsanity.com/paper/2401.01370/) (2023)
- [The Marine Debris Forward-Looking Sonar Datasets](https://mlsanity.com/paper/2503.22880/) (2025)
- [QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing](https://mlsanity.com/paper/2604.26435/) (2026)

## Abstract (arXiv)

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.