Traffic sign recognition is crucial for intelligent transportation and autonomous driving, as it can improve driving efficiency and ensure road safety. However, traditional recognition methods are based on large datasets and intensive computation, which limits their real-time applicability. Spiking Neural Networks (SNNs) offer a biologically inspired, energy-efficient alternative due to their spatiotemporal processing capabilities, but suffer from information loss and vanishing gradients during training. To overcome these limitations, this study proposes a Quantum Deep-supervised Spiking Neural Network (QDS-SNN) that integrates Quantum Neural Networks (QNNs) for efficient, low-power deep supervision. Using quantum superposition and entanglement, QNNs enable expressive representations and parallel computation, thereby enhancing performance without compromising energy efficiency. The proposed QDS-SNN incorporates a temporally and spatially adaptive LIF (TSA-LIF) neuron and a quantum-assisted classifier module (QACM) to mitigate gradient issues and improve training effectiveness. This study conducts experiments on the PennyLane quantum simulation platform, and the results show that QDS-SNN achieves 99.72\% accuracy on the GTSRB dataset in only 6 time steps -- outperforming the MS-ResNet baseline by 1.32\% while reducing energy consumption by 55.77\%. In the TSRD dataset, it achieves 97.90\% accuracy while reducing energy use to 52.68\% of the baseline. These results demonstrate that QDS-SNN offers a high-performance, energy-efficient solution for traffic sign recognition in intelligent transportation systems.

翻译：交通标志识别对于智能交通和自动驾驶至关重要，能够提升驾驶效率并保障道路安全。然而，传统识别方法依赖大规模数据集与密集计算，限制了其实时应用能力。脉冲神经网络（SNN）凭借其时空处理能力，提供了一种受生物启发的能效型替代方案，但在训练过程中存在信息损失和梯度消失问题。为克服这些局限，本研究提出一种集成量子神经网络（QNN）的量子深度监督脉冲神经网络（QDS-SNN），通过高效低功耗的深度监督机制实现性能提升。QNN利用量子叠加与纠缠特性，实现高表达能力表征与并行计算，在不牺牲能效的前提下增强模型性能。所提出的QDS-SNN采用时空自适应LIF神经元（TSA-LIF）与量子辅助分类模块（QACM），以缓解梯度问题并改善训练效果。本研究在PennyLane量子模拟平台上进行实验，结果表明：QDS-SNN在仅6个时间步长下，在GTSRB数据集上达到99.72%的准确率——比MS-ResNet基线模型高出1.32%，同时能耗降低55.77%。在TSRD数据集上，其准确率达97.90%，能耗降至基线的52.68%。这些结果证实，QDS-SNN为智能交通系统中的交通标志识别提供了高性能、高能效的解决方案。