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arXiv:2310.05307 · IET Quantum Communication

Successive data injection in conditional quantum GAN applied to time series anomaly detection

Benjamin Kalfon, Soumaya Cherkaoui, Jean‐Frédéric Laprade, Ola Suleiman Ahmad, Shengrui Wang

Published Feb 27, 2024• 14 citations
arXivHTMLPDF

Abstract

Abstract Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.

DOI
10.1049/qtc2.12088

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Abstract Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.
Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivariate time-series anomaly detection that leverages variational quantum circuits (VQCs) in combination with a time-window shifting technique, data re-uploading, and successive data injection (SuDaI). The method encodes multivariate time series data as rotation angles. By integrating both data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently, helping to address hardware limitations such as the restricted number of available qubits. In addition, the approach employs an anomaly scoring technique that utilizes both the generator and the discriminator output to enhance the accuracy of anomaly detection. The QGAN was trained using the parameter shift rule and benchmarked against a classical GAN. Experimental results indicate that the quantum model achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model. Notably, the QGAN accomplishes this performance with only 80 parameters, demonstrating competitive results with a compact architecture. Tests using a noisy simulator suggest that the approach remains effective under realistic noise-prone conditions.
Anomaly detection in time series is constrained by the scarcity and unpredictability of real anomalous events. Classical supervised and generative approaches struggle to capture the statistical complexity of rare, high-impact anomalies while maintaining realistic normal dynamics. This work introduces a Quantum Wasserstein Generative Adversarial Network (QWGAN) for synthetic anomaly generation that separates the modeling of normal and anomalous behaviors using quantum parameterized circuits. Leveraging quantum superposition and entanglement, the QWGAN achieves a richer generative capacity with an order-of-magnitude reduction in parameters compared to classical counterparts. The model is trained and evaluated on cyber security datasets, demonstrating lower Earth Mover’s Distance and volatility error while preserving temporal structure and distributional fidelity. A windowed synthesis mechanism enables controlled injection of variable-length point and contextual anomalies. Our results demonstrate that QWGAN provides a scalable, parameter-efficient approach for generating high-fidelity synthetic anomalous time series data to enable effective benchmarking and augmentation of anomaly detection models under limited anomaly data.
Abstract Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition, or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60–230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60-230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
Quantum Neural Networks (QNNs) offer a new paradigm that takes advantage of quantum computing principles to emulate and improve on the functional benefits offered by classical neural networks. This chapter offers a broad investigation on QNNs in the context of anomaly detection in important real-world settings such as cybersecurity, fraud detection in financial transactions, real-time medical diagnostics, and many more. This chapter will provide a general overview of QNNs, their theoretical background, advantages with respect to classical models, and application or implementation success in anomaly detection for high-dimensional, noisy and streaming data. The chapter will also cover architectures, training methodologies, and simulation platforms for QNNs, and also include case studies to demonstrate these concepts. Finally, it will address their limitations, challenges in practical implementations, and future research possibilities.
Quantum Kernel Methods for Anomaly Detection in High-Velocity Data Streams introduces a novel framework leveraging quantum computing principles to address critical challenges in real-time anomaly detection. By combining the expressive power of quantum-enhanced feature spaces with classical machine learning techniques, the work presents a hybrid architecture capable of identifying subtle anomalies in complex, high-dimensional streaming data. The framework incorporates specialized quantum feature maps that efficiently encode temporal and distributional properties of data streams, while adaptation mechanisms respond to concept drift and evolving patterns. Through systematic experimental evaluation across synthetic and real-world datasets from financial transactions, network security, and industrial systems, the approach demonstrates superior detection performance particularly for complex nonlinear patterns in high-dimensional spaces. The quantum-classical implementation addresses current hardware constraints through optimization techniques and targeted resource allocation, establishing specific conditions where quantum advantage emerges for operational anomaly detection scenarios.