Hybrid Classical-Quantum Autoencoder for Anomaly Detection

We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for an...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Sakhnenko, Alona, O'Meara, Corey, Ghosh, Kumar J B, Mendl, Christian B, Cortiana, Giorgio, Bernabé-Moreno, Juan
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 16.12.2021
Subjects:
Anomalies
Circuits
Data analysis
Data points
Data search
Datasets
Outliers (statistics)
Power plants
Predictive maintenance
ISSN:2331-8422
Online Access:Get full text
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Summary:We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for anomalous data points within a classical dataset. Using this model and applying it to both standard benchmarking datasets, and a specific use-case dataset which relates to predictive maintenance of gas power plants, we show that the addition of the PQC leads to a performance enhancement in terms of precision, recall, and F1 score. Furthermore, we probe different PQC Ans\"atze and analyse which PQC features make them effective for this task.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2112.08869