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arXiv · 2112.08869

Hybrid Classical-Quantum Autoencoder for Anomaly Detection

Alona Sakhnenko·Corey O'Meara·Kumar J. B. Ghosh·Christian B. Mendl·Giorgio Cortiana·Juan Bernabé-Moreno
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Abstract

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.

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Alona Sakhnenko, Corey O'Meara, Kumar J. B. Ghosh, Christian B. Mendl, Giorgio Cortiana, Juan Bernabé-Moreno. 2021-12-16. Hybrid Classical-Quantum Autoencoder for Anomaly Detection. https://doi.org/10.1007/s42484-022-00075-z

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