Applying quantum autoencoders for time series anomaly detection

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...

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Vydáno v:Quantum machine intelligence Ročník 7; číslo 1
Hlavní autoři: Frehner, Robin, Stockinger, Kurt
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.06.2025
Témata:
Artificial Intelligence
Computational Intelligence
Engineering
Quantum Information Technology
Research Article
Spintronics
Quantum computing
Quantum machine learning
Time series anomaly detection
Quantum autoencoder
ISSN:2524-4906, 2524-4914
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Shrnutí: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.
ISSN:2524-4906
2524-4914
DOI:10.1007/s42484-025-00285-1