QTopic: A novel quantum perspective on learning topics from text
Year: 2025
Type: article
Abstract: Topic modeling is an unsupervised technique in natural language processing (NLP) used to identify hidden topic structures within large text datasets. Among traditional approaches of topic modeling, latent Dirichlet allocation, BERTopic, and Top2Vec, are widely adopted to uncover hidden topics in text data. However, these methods often struggle with poor performance in scenarios involving limited data availability or high-dimensional textual features. In this research, we propose QTopic, a novel hybrid quantum-classical topic modeling architecture that leverages quantum properties through parameterized quantum circuits. By integrating quantum-enhanced sampling into the inference pipeline, the proposed model captures richer topic distributions by mapping textual data into a higher-dimensional space. Benchmark experiments demonstrate that QTopic consistently outperforms classical approaches in terms of coherence, diversity, and topic distinctiveness, particularly when modeling a small number of topics. This study demonstrates the promise of quantum techniques in advancing unsupervised NLP, while also highlighting hardware limitations that present challenges for future research.
Source: Neurocomputing
Authors: Monika Kabir, Mohammed Kaosar, Ferdous Sohel
Institution: Murdoch University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,670
OpenAlex ID: W4417526940