TBNQuantum
Predictive Maintenance at the Edge: An Embedded Anomaly Detection Pipeline Trained with Quantum Optimization
Publication: quant-ph updates on arXiv.org
Author: Filipe Chagas Ferraz
Source published: 02/11/2026, 05:00:00 UTC
Briefing created: 02/11/2026, 06:15:34 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 02/11/2026, 05:13:25 UTC.
Briefings
A new predictive maintenance pipeline detects bearing anomalies using vibration signals, adapted for edge deployment on low-cost microcontrollers. The system uses a one-class support vector machine (OCSVM) trained via variational quantum optimization, specifically employing variational quantum eigensolver (VQE) for feature selection and Pauli-correlation encoding (PCE) for classification. Initial small-scale tests on a simulator using the Paderborn bearing dataset showed VQE found the optimal feature selection in 36% of runs with Adam optimizer, while PCE selected better support vectors than chance until a classical local search equalized the results.
Key points
- The pipeline uses a one-class support vector machine (OCSVM) trained via variational quantum optimization to detect bearing anomalies in vibration signals.
- VQE found the optimal feature selection in 36% of runs with Adam optimizer on the Paderborn bearing dataset simulator.
Context
This research presents an embedded anomaly detection pipeline for predictive maintenance, adapted for deployment on low-cost microcontrollers. The method combines quantum optimization techniques with classical machine learning to optimize feature selection and classification for edge devices.
Limitations
Initial results were obtained from small-scale simulator tests using the Paderborn bearing dataset. Results are preliminary and may not reflect real-world performance on diverse hardware or operational conditions.
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