Quantum Autoencoder for Multivariate Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2504.17548v1
• Date: Thu, 24 Apr 2025 13:40:06 GMT
• Title: Quantum Autoencoder for Multivariate Time Series Anomaly Detection
• Authors: Kilian Tscharke, Maximilian Wendlinger, Afrae Ahouzi, Pallavi Bhardwaj, Kaweh Amoi-Taleghani, Michael Schrödl-Baumann, Pascal Debus,
• Abstract summary: Anomaly detection is a critical capability in IT security for recognizing incidents such as system misconfigurations, malware, or cyberattacks.<n>With the advent of quantum machine learning, many avenues open for dealing with such complex data.<n>We introduce a novel QAE-based framework designed specifically for time series AD towards enterprise scale.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP HANA Cloud systems, this task often involves monitoring high-dimensional, multivariate time series (MTS) derived from telemetry and log data. With the advent of quantum machine learning offering efficient calculations in high-dimensional latent spaces, many avenues open for dealing with such complex data. One approach is the Quantum Autoencoder (QAE), an emerging and promising method with potential for application in both data compression and AD. However, prior applications of QAEs to time series AD have been restricted to univariate data, limiting their relevance for real-world enterprise systems. In this work, we introduce a novel QAE-based framework designed specifically for MTS AD towards enterprise scale. We theoretically develop and experimentally validate the architecture, demonstrating that our QAE achieves performance competitive with neural-network-based autoencoders while requiring fewer trainable parameters. We evaluate our model on datasets that closely reflect SAP system telemetry and show that the proposed QAE is a viable and efficient alternative for semisupervised AD in real-world enterprise settings.

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