Quantum Topic Model: Topic Modeling Using Variational Quantum Circuits

Quantum machine learning aims to leverage quantum computing to enhance the computing capabilities and storage efficiency of classical machine learning. Among them, quantum generative models, as a type of unsupervised machine learning, have been proven to learn distributions that are outside of classical machine learning reach. However, there is limited work on how to truly utilize and validate a possible quantum advantage in practical applications, especially in the field of natural language processing. This paper proposes the first Quantum Topic Model (QTM) based on variational quantum circuits to validate and showcase the possible advantages of quantum computing for generative models. QTM not only generates random samples like generative models but also exhibits fitting capabilities similar to neural networks. Finally, we conducted experiments on real datasets, and the results showed that QTM achieved a 46.9% reduction in model parameters while obtaining 10.4% higher topic coherence and 0.2% higher topic diversity with fewer iterations than neural topic models.