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arXiv:2609.14025

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

Niloy Kumar Mondal, Poulomi Sarker Puja

Published Sep 12, 2026• 0 citations
arXivHTMLPDF

Abstract

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% $\mathrm{mAP}_{50}$ on Watertank with approximately $62\times$ fewer parameters than YOLO26s and $164.3\times$ fewer parameters than SSD-VGG16. On UATD, the model achieves 70.37% $\mathrm{mAP}_{50}$ with only 0.150M parameters, making it approximately $4.1\times$ smaller than SSGA-YOLO while retaining meaningful multi-class detection capability.

DOI
10.48550/arxiv.2609.14025

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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\mathrm{mAP}_{50}mAP50​ on Watertank with approximately 62×62\times62× fewer parameters than YOLO26s and 164.3×164.3\times164.3× fewer parameters than SSD-VGG16. On UATD, the model achieves 70.37% mAP50\mathrm{mAP}_{50}mAP50​ with only 0.150M parameters, making it approximately 4.1×4.1\times4.1× smaller than SSGA-YOLO while retaining meaningful multi-class detection capability.
The convergence between artificial intelligence (AI) and quantum computing is ushering in a new era for problems requiring high levels of computational complexity, particularly in digital signal processing. This paper presents an overview and evaluation of the development trends in hybrid Quantum-AI models, focusing on applications for the recognition and classification of underwater acoustic signals—a challenging domain due to instability, high noise levels, and data scarcity. The study proposes a Hybrid Quantum-CNN model, utilizing CNN for feature extraction and dimensionality reduction, combined with a Variational Quantum Classifier for classification in Hilbert space. The results of the proposed model are compared with an equivalent CNN network, demonstrating that HQC achieves higher accuracy (an increase of 3,5%), while excelling in efficiency: faster convergence speed, and superior generalization capabilities on the same real underwater acoustic dataset. These findings highlight the benefits of quantum approaches not only in accuracy but also in constructing more efficient and robust models; hybrid models open promising avenues for applying quantum machine learning to complex signal reception systems in practical.
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