ArticleScientific reports2026
Quantum-enhanced spiking intelligence framework for real-time anomaly detection in industrial internet of things.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Industrial Internet of Things (IIoT) anomaly detection imposes concurrent requirements for temporal consistency, computational latency control, and representation of high-dimensional heterogeneous sensor data. Addressing these constraints requires models capable of processing non-stationary streams while maintaining bounded inference time and preserving inter-sensor dependencies. The proposed Quantum-Enhanced Spiking Neural Network (QESNN) integrates parameterized quantum circuits with event-driven spiking computation within a unified inference structure. The architecture is comprised of five computation modules. The Quantum Neurons for Sensor Data Fusion module maps multimodal sensor observations onto amplitude-phase quantum states and then uses nonlinear unitary transformations to generate joint feature representations through multiple channels. The Entanglement-Based Device Synchronization module aligns phases between nodes by using entangled states, coherence-control operators, and correlations for synchronization. The Probabilistic Quantum Spiking for Anomaly Detection module uses spike probability based on tunneling and incorporates fidelity and entropy metrics to estimate the likelihood of anomalies under uncertainty. The Adaptive Quantum Learning for Dynamic Optimization module tunes model parameters via time-dependent Hamiltonian dynamics with annealing strategies and feedback correction schemes to ensure robust convergence in dynamic environments. Finally, the Quantum Decoherence Management for Reliable Processing module models the impact of noise, utilizes coherence-control operations, and conducts reliability checks in order to maintain stable quantum-state computation amidst external disturbances. When tested on the MVTec Anomaly Detection dataset, the model achieves classification accuracy of 98.7% with 512 logical quantum-spiking units and an inference time of 3.29 s. The effect of each component is estimated via ablation study - eliminating the Quantum Neurons for Sensor Data Fusion module leads to classification accuracy of 92.5%, whereas elimination of the Quantum Decoherence Management for Reliable Processing module produces the lowest classification accuracy of 88.9%.
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