Open Access

ARTICLE

Quantum Kernels for Text Classification: A Statistical and Diagnostic Framework Revealing the Low-Data Regime

Mrugendrasinh Rahevar¹, Martin Parmar¹, Hemant Yadav¹, Chun-Ta Li^2,*, Agbotiname Lucky Imoize³, Hiren Mewada⁴

1 C S Patel Institute of Technology, Charotar University of Science and Technology, Changa, Anand, India
2 Bachelor’s Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City, Taiwan
3 Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria
4 Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia

* Corresponding Author: Chun-Ta Li. Email:

Computer Modeling in Engineering & Sciences 2026, 148(2), 32 https://doi.org/10.32604/cmes.2026.085393

Received 10 May 2026; Accepted 29 June 2026; Issue published 28 August 2026

Abstract

Quantum kernel techniques aim to leverage quantum computational capabilities on social data. However, their application to natural language processing tasks faces formidable obstacles, such as extreme dimensionality reduction ($D=384\to k=8$), concentration of measure in quantum feature spaces, and the lack of theoretical understanding of when quantum advantages occur in kernel-based text classification. Filling this gap, we provide a comprehensive study of quantum kernels for text classification that addresses three major challenges in existing studies: general data compression approaches that ignore class structure, the lack of a predictive diagnostic toolkit, and overlooked approaches for handling concentration effects. Our main contributions include a supervised contrastive data compression approach with theoretical guarantees of kernel alignment, a five-diagnostic toolkit connecting theoretical insights with practical performance, the discovery of a small-data regime ($n\le 200$) in which quantum methods perform comparably to classical approaches, and a transparent demonstration that quantum kernels require carefully designed settings to remain competitive with classical counterparts. Across four datasets, evaluated using five random seeds and exact paired statistical testing, we observe that quantum projected kernels achieve an accuracy of $84.5\mathrm{\%}$ compared to $85.1\mathrm{\%}$ for the classical RBF kernel ($p>0.05$, not statistically significant) under supervised compression settings. However, quantum methods lag behind by approximately $2$%–$4\mathrm{\%}$ at larger scales due to concentration effects, reflected in reduced off-diagonal kernel variance ($0.003$ vs. $0.025$).

---

Keywords

---