用量子神经网络提升脉冲神经网络能效，6步完成交通标志识别
QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition
- 融合量子计算的脉冲神经网络，利用量子叠加与纠缠实现低功耗深度监督
- 在GTSRB数据集上达99.72%准确率，比基准模型高1.32%，能耗降低55.77%
- 适合智能交通、自动驾驶等对实时性与能效要求高的场景
交通标志识别对智能交通与自动驾驶至关重要，可提升行车效率并保障道路安全。然而，传统方法依赖大规模数据与高算力，难以满足实时需求。脉冲神经网络（SNN）因其时空处理能力具备生物启发的低功耗优势，但训练中存在信息丢失与梯度消失问题。为此，本文提出量子深度监督脉冲神经网络（QDS-SNN），通过量子神经网络（QNN）实现高效低功耗的深度监督。利用量子叠加与纠缠，QNN支持高表达力表征与并行计算，不牺牲能效前提下提升性能。所提QDS-SNN引入时空间自适应LIF（TSA-LIF）神经元与量子辅助分类模块（QACM），缓解梯度问题，增强训练效果。实验基于PennyLane量子仿真平台，在GTSRB数据集上仅用6个时间步即达到99.72%准确率，较MS-ResNet基线提升1.32%，能耗降低55.77%；在TSRD数据集上达97.90%准确率，能耗仅为基线的52.68%。结果表明，QDS-SNN为智能交通系统中的交通标志识别提供了高性能、低功耗的解决方案。
原文摘要 · Abstract (English)
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.
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