Anomaly detection plays a crucial role in identifying rare and critical events, anomalies, or outliers that are not part of the normal data distribution. With the large volume of modern data and the widespread use of sensors and IoT devices, datasets have evolved into high-dimensional structures, posing significant challenges to anomaly detection tasks. In this work, we investigate to employ a hybrid quantum-classical algorithm to help handle such challenges. It is mainly based on Local Outlier Factor (LOF) algorithm to detect anomalies. However, we enhance distance estimation and k-nearest neighbor search via quantum algorithms. The proof-of-principle numerical results demonstrate the performance of the proposed method is competitive to previous algorithm.
Anomaly detection plays a crucial role in identifying rare and critical events, anomalies, or outliers that are not part of the normal data distribution. With the large volume of modern data and the widespread use of sensors and IoT devices, datasets have evolved into high-dimensional structures, posing significant challenges to anomaly detection tasks. In this work, we investigate to employ a hybrid quantum-classical algorithm to help handle such challenges. It is mainly based on Local Outlier Factor (LOF) algorithm to detect anomalies. However, we enhance distance estimation and k-nearest neighbor search via quantum algorithms. The proof-of-principle numerical results demonstrate the performance of the proposed method is competitive to previous algorithm.
Anomaly detection, the task of identifying rare and unusual events within a dataset, is a critical component in numerous applications, including fraud detection, cybersecurity, and industrial monitoring. Traditional anomaly detection methods often rely on classical machine learning algorithms which can be sensitive to noise and may struggle with complex, high-dimensional data. This paper investigates the application of quantum machine learning (QML) algorithms to enhance anomaly detection performance. We propose a framework where anomaly detection problems are formulated as quantum machine learning tasks, leveraging quantum algorithms for data analysis and pattern recognition. The core claim of this work is that QML can significantly improve the precision and efficiency of anomaly detection compared to conventional techniques. Specifically, we explore the potential of quantum support vector machines (QSVMs) and quantum clustering algorithms for anomaly identification. The theoretical underpinnings of these approaches are outlined, and we present a conceptual model demonstrating the transformation of a classical anomaly detection problem into a quantum equivalent. This work lays the groundwork for future research in applying QML to real-world anomaly detection challenges.
This research explores the integration of quantum computing and machine learning for anomaly detection in critical infrastructure sensor networks. Anomaly detection is crucial for maintaining the reliability and security of these networks, where early detection of faults or intrusions can prevent catastrophic failures. Traditional machine learning methods often struggle with the high-dimensional and complex data generated by these systems. Hybrid quantum approaches, combining quantum algorithms with classical machine learning models, offer a promising solution by leveraging quantum computational advantages, such as quantum feature mapping and kernel methods. This paper investigates how these hybrid models can improve detection accuracy, reduce processing time, and handle large-scale data more efficiently. The potential of quantum machine learning for anomaly detection in critical infrastructure is discussed, along with the challenges posed by current quantum hardware limitations and future directions for research.
Anomaly detection is a prominent task in machine learning and data mining, which plays a key role in various domains such as financial fraud detection, network intrusion detection, and healthcare. Angle-based outlier detection (ABOD) algorithm stands out as one of the most common approaches for anomaly detection, the core step of which involves computing the angular variance among distance vectors from each point to others, and then marking those points with angular variance lower than a threshold as outliers. The time complexity of classical ABOD algorithm scales as mathcalOM³) with respect to the number of samples M, making it hard to efficiently handle large-scale datasets. To this end, a quantum ABOD algorithm is proposed, in which quantum amplitude estimation is harnessed to compute angular variance in quantum parallel, and quantum amplitude amplification is further applied to search out the outliers. Compared with its classical counterpart, our quantum ABOD algorithm has time complexity Ołeft[\sqrt{M}\log(M)\right] with respect to M, achieving a polynomial speedup with respect to M.
The integration of Quantum Computing and Machine Learning (QML) holds transformative potential for intelligent data analysis, especially in processing high-dimensional and complex datasets. This paper proposes a hybrid quantum-classical framework for proactive anomaly detection, combining Quantum Auto-Encoders (QAEs) for feature compression with Quantum k-Nearest Neighbors (QkNN) for classification. Tailored for high-stakes domains like cybersecurity and IoT analytics, the approach leverages quantum entanglement and exponential state-space properties to enhance anomaly identification accuracy and efficiency. QAEs capture latent representations of typical behavior, while QkNN applies quantum parallelism to classify deviations against known patterns. Evaluations on standard benchmark datasets demonstrate the proposed QAE+QkNN framework’s superiority in precision, scalability, and resilience, particularly under noisy and sparse data conditions. While current limitations in quantum hardware pose constraints, the results underscore the method’s practical viability and promise. This study contributes to the growing body of QML research and highlights the potential of quantum-augmented detection systems in enabling the next generation of real-time, intelligent monitoring solutions.
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
Anomaly detection (AD) underpins fraud prevention, network security, industrial condition monitoring, medical diagnosis, and searches for new physics. However, it remains challenging due to rare anomalies, scarce labeled data, and the high dimensionality and strong correlation of sensor data. Many classical machine learning (ML) algorithms are designed to address AD in various settings, but the growth of quantum computing opens a new path for these problems. Quantum machine learning (QML) offers new tools, from exploring exponentially large Hilbert spaces and promising speed-ups to the native processing of quantum data. Thus, this review connects the two fields. We first present the basics of quantum computing, quantum algorithms, and QML with the most influential literature, and formalize the classical anomaly scores that quantum methods inherit. We then systematically review 30 primary studies of quantum anomaly detection (QAD) published between 2018 and 2026, express each method family through its anomaly score, and organize the field into a taxonomy of six families: quantum kernel methods, quantum autoencoders, quantum generative models, variational quantum classifiers, quantum linear-algebra methods, and detectors for quantum data. A cross-study analysis shows that the field has moved from asymptotic speed-up proposals to small hybrid experiments, that kernels and autoencoders dominate, that only nine studies run on gate-based hardware, and that none yet demonstrates a robust, practically relevant advantage over well-tuned classical detectors. We close with the main obstacles, such as data embedding, barren plateaus, kernel concentration, and weak baselines, and a research roadmap that prioritizes quantum data, fair benchmarks against classical baselines, and robust detectors for sequential and distributed data.