Hybrid quantum-classical machine learning for industrial multi-anomaly detection via single acoustic sensor.

We developed a novel workflow that leverages Quantum Kernel feature space expressiveness combined with classical dimensionality reduction techniques. This workflow enables detection and visual identification of multiple simultaneous anomalies using acoustic data from a single non-contact sensor. Our newly developed method provides an intuitive interface for operators to identify specific anomalies. By combining conventional Mel-frequency cepstral coefficients (MFCC) with principal component analysis (PCA), the newly constructed Quantum Kernel achieves near-perfect classification (F1 = 1.0 under specific conditions: j ≥ 9 features, file-level data splits) of complex multi-source anomalies, significantly outperforming the classical RBF kernel (F1 ≈ 0.75-0.76) and 1D-CNN autoencoder baseline (F1 ≈ 0.60-0.85). The Quantum Kernel's superior representational power enables accurate anomaly detection with minimal training data. Our results demonstrate simulation-based evidence of potential quantum advantage under ideal conditions, where the performance of our method dramatically exceeds that of classical approaches as the number of features increases, pending validation on noisy intermediate-scale quantum (NISQ) hardware. This hybrid quantum-classical machine learning approach demonstrates significant potential for industrial applications, particularly for complex time-series data in data-scarce regimes where classical methods exhibit limited performance.