Hybrid Classical-Quantum Autoencoder for Anomaly Detection

• URL: http://arxiv.org/abs/2112.08869v1
• Date: Thu, 16 Dec 2021 13:27:24 GMT
• Title: Hybrid Classical-Quantum Autoencoder for Anomaly Detection
• Authors: Alona Sakhnenko, Corey O'Meara, Kumar J. B. Ghosh, Christian B. Mendl, Giorgio Cortiana, Juan Bernab\'e-Moreno
• Abstract 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) 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. We show that the addition of the PQC leads to a performance enhancement in terms of precision, recall, and F1 score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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