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Optics and Photonics News

Research and Industry News

Edwin Cartlidge, Patricia Daukantas, Meeri Kim, Sarah A. Michaud, Karen Kwon, Stewart Wills, Alessia H. Kirkland

Published Dec 1, 2021• 0 citations
Publisher

Abstract

Toward fault-tolerant quantum computing / Detecting solar-panel defects / Fluorescent dyes encode data / Low-noise LIGO mirror coatings / High-performance photodetectors / X-ray scattering / Europe’s quantum tech / Advancing lidar market / Cooking with lasers / Upgraded comb breathalyzer
DOI
10.1364/opn.32.12.000008

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Class imbalance remains a major challenge in industrial condition monitoring and fault classification, where minority fault patterns can be poorly represented during machine learning. This work develops a noise-aware quantum-inspired oversampling framework and systematically evaluates its effectiveness and robustness for industrial and energy-system fault diagnosis. The principal artificial-intelligence contribution is a noise-aware extension of the Quantum Synthetic Minority Oversampling Technique (QSMOTE), in which quantum-inspired perturbations are introduced directly into the compact-swap-test-based similarity estimation used during synthetic minority-sample generation. Consequently, noise propagates through overlap estimation and angle computation to the generated samples, enabling direct assessment of the robustness of the oversampling mechanism rather than perturbing only the downstream classifier. The engineering application is evaluated on four public datasets covering solar-panel faults, bearing condition monitoring, engine failures, and industrial fault detection. Five classical classifiers are used to compare performance on the original imbalanced data, after QSMOTE-based balancing, and under noisy-QSMOTE. The results show substantial classifier-dependent benefits from QSMOTE, including improvements of up to 170% on the engine-failure dataset and accuracy exceeding 0.99 on the industrial fault-detection dataset. Robustness experiments across six quantum-inspired noise channels further demonstrate that perturbations in similarity estimation can produce markedly different downstream effects depending on the noise model and classifier. The proposed framework therefore provides a reproducible approach for jointly assessing class-imbalance mitigation and noise robustness of quantum-inspired oversampling in practical industrial artificial-intelligence applications.