Anomaly detection is a crucial task in machine learning that involves identifying unusual patterns or events in data. It has numerous applications in various domains such as finance, healthcare, and cybersecurity. With the advent of quantum computing, there has been a growing interest in developing quantum approaches to anomaly detection. After reviewing traditional approaches to anomaly detection relying on statistical or distance-based methods, we will propose a Quadratic Unconstrained Binary Optimization (QUBO) model formulation of anomaly detection, compare it with classical methods, and discuss its scalability on current Quantum Processing Units (QPU).
Anomaly detection is a crucial task in machine learning that involves identifying unusual patterns or events in data. It has numerous applications in various domains such as finance, healthcare, and cybersecurity. With the advent of quantum computing, there has been a growing interest in developing quantum approaches to anomaly detection. After reviewing traditional approaches to anomaly detection relying on statistical or distance-based methods, we will propose a Quadratic Unconstrained Binary Optimization (QUBO) model formulation of anomaly detection, compare it with classical methods, and discuss its scalability on current Quantum Processing Units (QPU).
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.
In this work, we introduce a hybrid quantum-classical framework to address anomaly detection problems in time series data using an innovative Quadratic Unconstrained Binary Optimization formulation. The proposed approach integrates density-based and statistical methodologies with the Quantum Approximate Optimization Algorithm, providing a versatile tool for anomaly detection. Moreover, the underlying model is transversal to different kinds of anomalies and can be directly applied to diverse applications, ranging from fault detection to novelty discovery. Experimental results prove that this architecture achieves competitive accuracy compared to classical techniques, with the unique advantage of exploring alternative solutions in parallel due to the properties of quantum computing. This capability enables a deeper understanding of patterns in time series data.
The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The review provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The review provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.
Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently emerged as a powerful tool for tackling several machine learning tasks, but training quantum machine learning models remains challenging, particularly due to the difficulty of gradient calculation. The challenge is even greater for anomaly detection, where unsupervised learning methods are essential to ensure practical applicability. To address these issues, we propose Quorum, the first quantum anomaly detection framework designed for unsupervised learning that operates without requiring any training.
This paper introduces a novel approach to anomaly detection in time series data inspired by quantum mechanics, specifically quantum superposition and interference. Traditional anomaly detection methods often struggle with subtle deviations that may be masked within noisy data or require significant domain expertise to identify. Our method leverages the inherent properties of quantum states to represent time series data, enabling the generation of interference patterns that can amplify weak anomalies. We formulate the detection process as a measurement problem within a quantum framework, where the act of measurement itself can accentuate deviations from the expected quantum state. The core idea is to represent the time series as a superposition of possible states, and then observe the interference patterns resulting from this superposition. Anomalous events cause a shift in the interference pattern, which can be detected with higher sensitivity than traditional statistical methods. This approach offers the potential to significantly improve anomaly detection performance, particularly in scenarios with complex, high-dimensional time series data. The theoretical framework is presented, followed by a discussion of the key mathematical concepts and operational considerations. The paper concludes with a summary of the proposed method and its potential future directions.
Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains, including healthcare, cybersecurity, finance, and energy. Data augmentation and generative AI may mitigate training-data scarcity, but they often fall short because anomalies are, by definition, unpredictable, rare, and highly diverse events compared to high-probability normal data. Overfitting to pseudo-anomalies, model collapse, high-dimensional data, uninterpretable black-box models, and validation challenges are typical issues limiting their practical applicability. In this context, quantum machine learning may provide a promising and more sustainable avenue because it can enable more interpretable models with far fewer trainable parameters and smaller datasets, implementable on energy-efficient quantum hardware. Here, we propose a novel two-step hybrid classical--quantum architecture for sequential data and test it on a realistic scenario in the global energy-transition domain, i.e., automated anomaly detection in large-scale photovoltaic plants. The achieved generalization capability and competitive prediction accuracy may pave the way for new hybrid learning models able to exploit the continuously increasing power of cloud-available and more sustainable quantum accelerators integrated with more traditional energy-hungry High Performance Computing resources.