Quantum Kernel Methods for Industrial Anomaly Detection

Takao Tomono

Keywords: Quantum machine learning, Anomaly detection, Quantum kernels, Support vector machines, Industrial inspection, Image processing

Abstract: Anomaly detection plays a vital role in industrial quality control and manufacturing processes. Traditional machine learning methods often face challenges, especially in scenarios where training data is limited. In these circumstances, quantum machine learning (QML) has emerged as a promising approach to improve anomaly detection capabilities. This paper provides a comprehensive review of QML applications in industrial anomaly detection, with particular focus on image-based inspection systems, presenting our novel contributions. This paper classifies various types of anomalies encountered in industrial environments and provides a detailed review of classical and quantum anomaly detection approaches. In addition, we present the latest advances in quantum kernel methods in image-based anomaly detection. The analysis includes experimental results showing that quantum kernels outperform classical methods in certain industrial applications. For example, in shipment inspection, compared to an F1 score of 0.964 for SVM using an imbalanced dataset of 400 samples (300 normal, 100 anomalous), QSVM achieved an F1 score of 0.990 compared to 0.958 for ResNet (1132 normal), a 2.7% improvement in detection performance.　We also discuss the implementation of quantum support vector machines (QSVM) with quantum kernels and their performance on quantum simulators and actual quantum hardware. Hardware validation reveals that quantum circuits with depths ≤32 maintain consistent performance between simulators and actual quantum devices, while circuits with depths >273 suffer significant degradation (AUC: 0.89→0.59) due to noise accumulation. These findings establish practical guidelines for deploying quantum machine learning in industrial settings and provide a roadmap for future quantum-enhanced manufacturing systems.

Submission Number: 7