Hybrid Classical-Quantum Autoencoder for Anomaly Detection

TL;DR

This paper introduces a hybrid classical-quantum autoencoder that enhances anomaly detection by integrating a parametrized quantum circuit into the latent space, improving performance on benchmark and real-world datasets.

Contribution

The paper presents a novel hybrid autoencoder architecture combining classical and quantum components, demonstrating improved anomaly detection performance and analyzing quantum circuit features.

Findings

Enhanced precision, recall, and F1 score with the quantum augmentation

Effective PQC Ansätze identified for anomaly detection

Successful application to both benchmark and real-world datasets

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.