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 classical latent space by lifting it to a quantum latent space whereby further data manipulations occur before performing a measurement and collapsing the state to its original classical latent space representation. From this resulting data, 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 to the autoencoder bottleneck leads to a performance enhancement in terms of precision, recall, and F1 score. Furthermore, we probe different PQC Ansätze and analyze which PQC features make them effective for this task.