qds-snn-quantum-deeply-supervised-spiking

name	qds-snn-quantum-deeply-supervised-spiking
description	Quantum Deeply-Supervised Spiking Neural Network (QDS-SNN) methodology for energy-efficient traffic sign recognition. Integrates QNNs with SNNs using TSA-LIF neurons and QACM module, achieving 99.72% accuracy with 55.77% energy reduction.
version	1.0.0
author	Hermes Agent (Cron Job)
created	2026-06-09T00:00:00.000Z
source	arXiv:2606.07657
category	quantum-neuromorphic
tags	["quantum-neural-network","spiking-neural-network","deep-supervision","energy-efficient","traffic-sign-recognition","TSA-LIF","QACM"]
activation_keywords	["qds-snn","quantum spiking","deeply supervised snn","energy efficient recognition","traffic sign quantum","tsa-lif neuron"]

QDS-SNN: Quantum Deeply-Supervised Spiking Neural Network

Overview

QDS-SNN integrates Quantum Neural Networks (QNNs) with Spiking Neural Networks (SNNs) to overcome information loss and gradient vanishing in traditional SNN training, achieving high accuracy with significant energy efficiency improvements.

Key Performance:

• 99.72% accuracy on GTSRB dataset (German Traffic Sign Recognition Benchmark)
• 55.77% energy reduction compared to baseline
• 97.90% accuracy on TSRD dataset with 52.68% energy consumption of baseline
• 6 time steps for inference (very fast)

Core Methodology

1. Architecture Components

A. TSA-LIF Neuron (Temporally and Spatially Adaptive LIF)

• Purpose: Adaptive spiking neuron that adjusts temporal and spatial parameters dynamically
• Benefits:
  • Mitigates gradient vanishing in deep SNN layers
  • Enhances information propagation through temporal dynamics
  • Adaptive threshold adjustment for better spike generation

B. Quantum-Assisted Classifier Module (QACM)

• Purpose: Uses quantum circuits for classification
• Mechanism:
  • Leverages quantum superposition for expressive representations
  • Utilizes quantum entanglement for parallel computation
  • Enhanced feature extraction without computational overhead

2. Deep Supervision Strategy

• Multi-level supervision: Loss functions applied at intermediate layers
• Gradient flow improvement: Prevents vanishing gradients in deep networks
• Training efficiency: Faster convergence with better feature learning

3. Energy Efficiency Mechanism

• SNN sparsity: Only spikes transmit information (event-driven)
• Quantum parallelism: Reduced computational steps via quantum operations
• Adaptive firing: TSA-LIF reduces unnecessary spikes

Implementation Workflow

Step 1: Data Preprocessing

# Traffic sign image preprocessing
import cv2
import numpy  np

 ():
    
    image = cv2.resize(image, (, ))
    
    image = image / 
    
     image