QTopic: A novel quantum perspective on learning topics from text

Monika Kabir; Mohammed Kaosar; Ferdous Sohel

doi:10.1016/j.neucom.2025.132483

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QTopic: A novel quantum perspective on learning topics from text

Journal article

Open access

Peer reviewed

QTopic: A novel quantum perspective on learning topics from text

Monika Kabir, Mohammed Kaosar and Ferdous Sohel

Neurocomputing (Amsterdam), Vol.669, 132483

2026

DOI: https://doi.org/10.1016/j.neucom.2025.132483

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Abstract

Document clustering

Latent dirichlet allocation

Quantum natural language processing

Quantum topic modeling

Topic modeling is an unsupervised technique in natural language processing (NLP) used to identify hidden topic structures within large text datasets. Among traditional approaches to 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.

Details

Title: QTopic: A novel quantum perspective on learning topics from text
Authors/Creators: Monika Kabir - Murdoch University
Mohammed Kaosar - Murdoch University, School of Information Technology
Ferdous Sohel - Murdoch University, School of Information Technology
Publication Details: Neurocomputing (Amsterdam), Vol.669, 132483
Publisher: Elsevier B.V.
Number of pages: 10
Identifiers: 991005845959907891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Journal article

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Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.48 Knowledge Engineering & Representation; 4.48.672 Natural Language Processing
Web Of Science research areas: Computer Science, Artificial Intelligence
ESI research areas: Computer Science