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QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition

arXiv 2606.07657 ↗First posted 3 Jun 2026cs.NE cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 52 references that are in the map. First posted on arXiv 3 Jun 2026.

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References · 19

  1. 2017Squeeze-and-Excitation Networkscited by 4,763
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  3. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  4. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  5. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 263
  6. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  7. 2023Spike-driven Transformercited by 141
  8. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 111
  9. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  10. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 76
  11. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 39
  12. 2024Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networkcited by 19
7 more
  1. 2022Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognitioncited by 12
  2. 2025DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networkscited by 8
  3. 2025TS-SNN: Temporal Shift Module for Spiking Neural Networkscited by 5
  4. 2021An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networkscited by 4
  5. 2024A Quantum Leaky Integrate-and-Fire Spiking Neuron and Networkcited by 4
  6. 2020Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehiclecited by 3
  7. 2025Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networkscited by 1

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