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IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Dynamic Multi-Pqc Quantum Convolutional Neural Network for Real-Time Pothole Detection: Invited Paper

Minjoo Kim, Juhui Heo, Emily Jimin Roh, Soohyun Park

Published Jul 7, 2025• 1 citation
Publisher

Abstract

This paper presents a novel quantum artificial intelligence model for efficient and accurate pothole detection on road surfaces, specifically tailored for autonomous vehicle (AV) environments. Potholes are widely recognized in the literature as critical risk factors that can cause significant vehicle damage and contribute to traffic accidents. Accordingly, timely and precise detection of potholes is essential to ensure the safety of both vehicles and their occupants. To meet the stringent real-time constraints of AV navigation, the proposed approach employs quantum convolutional neural networks (QCNNs), which provide enhanced expressiveness, low-latency inference, and fewer parameters while achieving performance comparable to classical convolutional neural networks (CNNs). To ensure algorithmic stability under various road and traffic dynamics, a Lyapunov optimization strategy is integrated into the QCNN architecture. This enables the design of a stabilized learning framework, which dynamically regulates the detection model to maintain robustness and performance even in fluctuating environments. Experiments show that the proposed QCNN outperforms classical CNNs in both accuracy and efficiency. In addition, the proposed Lyapunov optimization-based QCNN architecture control further enhances real-time performance, considering inference delay.

DOI
10.1109/spawc66079.2025.11143329

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