LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)


  • Joseph A. Gallego-Mejia Universidad Nacional de Colombia
  • Oscar A. Bustos-Brinez Universidad Nacional de Colombia
  • Fabio A. González Universidad Nacional de Colombia




Anomaly Detection, Deep Learning, Density Matrix, Random Features, Quantum Machine Learning


This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, that learns a low-dimensional representation of the data, with a density-estimation model based on density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method is able to outperform other state-of-the-art methods.




How to Cite

Gallego-Mejia, J. A., Bustos-Brinez, O. A., & González, F. A. (2023). LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16210-16211. https://doi.org/10.1609/aaai.v37i13.26965