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November 25, 2025Open Access

Quantum-Enhanced Spiking Neural Network on FPGA for Real-Time Industrial Anomaly Detection

Deenathayalan A Tanushree RG Keertana P

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Key Points

The aim is to develop a quantum-inspired spiking neural network for real-time anomaly detection in industrial motors.
Implemented a novel quantum-inspired learning rule (Q-STDP) on FPGA.
Converted IMAD sensor recordings to spike trains for preprocessing.
Evaluated performance through classification accuracy and latency metrics.
Achieved 92.12% classification accuracy and 100% precision.
Mean latency was 4.10 ms with a throughput of 246.3 samples/s.
Demonstrated low power consumption at 265 mW per inference.

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Implication

Demonstrates effective anomaly detection in industrial motors, suggesting low-cost solutions for predictive maintenance.

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Cite This Study

A et al. (2025) studied this question.

synapsesocial.com/papers/6a06b95be7dec685947ac049 https://doi.org/10.5281/zenodo.20140111

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Quantum-enhanced spiking intelligence framework for real-time anomaly detection in industrial internet of thingsOriginal Research · 2026 · 1 citations
2HAPQ: A Hardware-Aware Pruning and Quantization Pipeline for Event-Based SNN DetectionOriginal Research · 2026
3Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning2025
4Q-SNNs: Quantized Spiking Neural Networks2024
5Embedded event based object detection with spiking neural network2024

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