Kankeu and Colleagues Present Hybrid Variational Autoencoder for Neural Topic Modeling

Ivan Kankeu, Institute for Quantum Studies, University of the West of Scotland, and colleagues have created a new approach to neural topic modelling using a hybrid classical-quantum variational autoencoder. The method integrates quantum circuits into a variational autoencoder, achieving improved performance on the AgNews dataset with a Cv coherence score of 0.71 and an NPMI score of 0.20. The model operates effectively on a low-resource 10-qubit quantum device, suggesting hybrid quantum-classical models offer a promising pathway for quantum-enhanced natural language processing even with the limitations of current noisy intermediate-scale quantum (NISQ) technology. A Cv coherence score of 0.71 on the AgNews dataset signifies a substantial improvement for Dr. Eleanor Rieffel and Dr. Markus Hollenbach’s hybrid classical-quantum variational autoencoder (VAE). Previously, topic models struggled…