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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Podrobná bibliografie
Vydáno v:	arXiv.org
Hlavní autoři:	Sakhnenko, Alona, O'Meara, Corey, Ghosh, Kumar J B, Mendl, Christian B, Cortiana, Giorgio, Bernabé-Moreno, Juan
Médium:	Paper
Jazyk:	angličtina
Vydáno:	Ithaca Cornell University Library, arXiv.org 16.12.2021
Témata:	Anomalies Circuits Data analysis Data points Data search Datasets Outliers (statistics) Power plants Predictive maintenance
ISSN:	2331-8422
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2112.08869