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.
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.
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.
Anomaly detection, an important task in data analysis, involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden patterns and deviations in large-scale data. The potential of quantum computing for machine learning has been widely recognized, leading to extensive research efforts to develop suitable quantum machine learning (QML) algorithms. In particular, the search for QML algorithms for near-term NISQ devices is in full swing. However, NISQ devices pose additional challenges due to their limited qubit coherence times, low number of qubits, and high error rates. Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints. Especially support vector machines (SVM) utilizing quantum kernel estimation have shown success in various supervised learning tasks. However, in the context of anomaly detection, semisupervised learning is of great relevance, and yet there is limited research published in this area. This paper introduces an approach to semisupervised anomaly detection based on the reconstruction loss of a support vector regression (SVR) with quantum kernel. This novel model is an alternative to the variational quantum and quantum kernel one-class classifiers, and is compared to a quantum autoencoder as quantum baseline and a SVR with radial-basis-function (RBF) kernel as well as a classical autoencoder as classical baselines. The models are benchmarked extensively on 10 real-world anomaly detection data sets and one toy data set and it is shown that our SVR model with quantum kernel performs better than the SVR with RBF kernel as well as all other models, achieving highest mean AUC over all data sets. In addition, our QSVR outperforms the Quantum autoencoder on 9 out of 11 data sets.
In cybersecurity space, accurate and efficient identification of network anomalies is crucial in protection of information systems and data. The purpose of this research is to present QSVM for improving the detection of anomalies in network traffic using the NSL-KDD dataset. The work uses QSVM and applies different kernel functions such as a linear kernel, a polynomial kernel, a radial basis function (RBF) kernel, and a sigmoid kernel to analyze the effects on QSVM. The degree to which each kernel can draw decision boundaries is plotted, which offers an understanding of how well the paradigm performs in identifying networks anomalies. Quantum computing is a useful application as it provides means for the improvement of the limiting factors of involving machine learning in security, and this work could help increase the efficiency of the realized methods in detecting anomalies. The outcomes of this research study are believed to stimulate more study of the interaction between quantum computing and network security
Reliable anomaly detection in subsea infrastructure is critical, as overlooked faults may result in severe environmental damage and substantial financial losses. Although classical machine learning approaches provide effective solutions, they often require significant computational resources and may struggle to capture the complex, high-dimensional correlations present in multivariate sensor data. In this work, we present a comprehensive evaluation of quantum and hybrid quantum–classical architectures using the 3W dataset, a large-scale industrial benchmark comprising 2,184 real and simulated subsea events. We develop an end-to-end pipeline that transforms raw multivariate time-series signals into quantum-compatible feature representations. Four hybrid architectures are systematically compared against six classical baselines in both supervised and unsupervised settings. Our results demonstrate that a compact hybrid model (Hybrid-A) achieves an F1-score of 0.9737 using only 177 trainable parameters, matching the performance of a 10,000-parameter Random Forest and exhibiting substantially higher parameter efficiency than conventional neural networks. In the unsupervised scenario, the proposed Quantum Autoencoder outperforms its classical counterpart, improving F1-score by 5.35% and ROC-AUC by 15.28% while reducing parameter count by 42%. These findings indicate that quantum-enhanced models can achieve competitive performance with significantly reduced model complexity, highlighting their potential for deployment in resource-constrained, safety-critical edge environments. Keywords: Quantum Machine Learning, Industrial Anomaly Detection, Hybrid Quantum-Classical Models, 3W Dataset, Edge Deployment, Parameter Efficiency
Outgoing inspection of the production line is very important for many manufacturing companies. In recent years, two class classification of good and defective products must be carried out efficiently as the number of small-quantity, high-mix products have been increasing. The support vector machine with kernel method (kernel learning) is the most popular method in two class classification used in factories. Quantum kernel learning is one of the most promising applications of quantum technology. In this study, we propose an analysis method that allows us to know the difference between classical and quantum machine learning by plotting false positive rate (FPR) and true positive rate (TPR) on Receiver Operating Characteristic (ROC) space. Quantum machine learning uses quantum data with feature maps. We compared quantum and classical data using a small-sized existing datasets. First, it was not possible to confirm that the quantum data incorporating quantum entanglement could achieve the effect in this study. Next, when we examined the learning process of quantum data with Pauli feature map and classical data, we observed differences in the initial learning process. Based on these results, in contrast to the commonly used ROC curve, we plotted the FPR and TPR separately on the ROC space according to the training size. Plotting on ROC space in this experiment shows that quantum kernel learning is a method to reduce only FPR from high FPR and TPR. We found that the kernel learning process using quantum data is different from one using classical data.
Early and accurate anomaly detection in Intensive Care Unit (ICU) monitoring is vital for improving patient outcomes and reducing mortality. Traditional machine learning techniques often face challenges in handling the noisy, high-dimensional, and limited datasets characteristic of ICU physiological signals. In this study, we present an early investigation of a quantum-enhanced framework employing a classically simulated Quantum Support Vector Machine (QSVM) to improve the detection of critical anomalies in real-world ICU time-series data. Utilizing the publicly available PhysioNet 2012 Challenge dataset, we extract comprehensive statistical and temporal features from vital signs including heart rate, blood pressure, respiratory rate, and oxygen saturation. Our QSVM model leverages a quantum kernel mapped via Qiskit’s ZZFeatureMap and is rigorously benchmarked against classical machine learning classifiers such as Support Vector Machines with radial basis function kernels, Random Forest, and XGBoost. Experimental results demonstrate that the QSVM shows competitive and robust classification performance, particularly excelling in scenarios with limited and noisy data, with an AUC of 0.92, particularly excelling in scenarios with limited and noisy data. Furthermore, the quantum kernel’s ability to implicitly project data into a high-dimensional Hilbert space provides enhanced separability of complex physiological patterns. This work constitutes one of the first simulation-based applications of quantum machine learning to critical care anomaly detection, underscoring the promise of hybrid quantum-classical approaches for advancing real-time clinical decision support systems.