Quantum Kernel Methods for Industrial Anomaly Detection

Anomaly detection plays a vital role in industrial quality control and manufacturing processes. Traditional machine learning methods face significant challenges when training data is limited, particularly in specialized manufacturing contexts. Quantum machine learning (QML) offers a promising approach to enhance anomaly detection capabilities under these constraints. 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. In shipment inspection using an imbalanced dataset of 400 samples (300 normal, 100 anomalous), QSVM achieved an F1 score of 0.990 compared to 0.964 for classical SVM and 0.958 for ResNet, representing 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.