Potential of multi-anomalies detection using quantum machine learning

Machine learning is widely used for industrial anomaly detection; however, when anomaly distributions overlap, high false positive rates (FPR) often occur, hindering practical implementation. While quantum kernel methods have been proposed as a solution, the specific conditions under which they outperform conventional methods remain unclear. This study investigated the conditions under which quantum kernels outperform the classical RBF kernel in industrial acoustic anomaly detection. Using autoregressive (AR) features and an unsupervised One-Class SVM, we compared a conventional radial basis function (RBF) kernel with two types of quantum kernels: QK1 (employing linear entanglement) and QK2 (employing all-to-all entanglement). Two datasets with distinct characteristics were used for the experiments: “Open Belt Drive (OBD),” where the anomaly class is separable, and “Mini 4WD (M4W),” where anomaly distributions overlap. While all methods achieved perfect classification for the OBD dataset, the quantum kernels reached optimal performance using fewer features. In contrast, for the M4W dataset, QK2 significantly outperformed the RBF kernel, achieving an F1 score of 0.893 and an FPR of 0.033 (compared to 0.428 and 0.600 for RBF, respectively). t-SNE analysis confirmed that the M4W anomaly data were difficult to separate in the conventional feature space. These results suggest that quantum kernels offer the greatest advantage in problems involving overlapping distributions and that analyzing separability via t-SNE helps identify anomaly detection tasks suitable for the application of quantum technology.