International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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Quantum Natural Language Processing Using Hybrid AI Algorithms: A Variational Quantum–Classical Framework for Efficient Text Classification

Author(s) Dr. P. U. ANITHA
Country India
Abstract Quantum Natural Language Processing (QNLP) represents a novel interdisciplinaryparadigm that integrates Quantum Computing with Natural Language Processing toenhance semantic modeling and computational efficiency. Despite the remarkable successof transformer-based architectures such as BERT and GPT, classical NLP systems requirelarge-scale computational resources and extensive parameter tuning. This paper proposesa hybrid quantum–classical architecture employing parameterized quantum circuits (PQCs)integrated with classical embedding layers for text classification tasks. The frameworkutilizes classical preprocessing, quantum state encoding, variational optimization, andclassical post-measurement classification. Experimental results on benchmark sentimentanalysis datasets demonstrate competitive accuracy with significantly reduced parametercomplexity. The findings indicate that hybrid QNLP models provide enhanced semanticcompositionality and efficient representation in Hilbert space while remaining feasiblewithin Noisy Intermediate-Scale Quantum (NISQ) constraints
Keywords Quantum Natural Language Processing; Hybrid AI Algorithms; Variational Quantum Circuits; Quantum Machine Learning; Text Classification; Sentiment Analysis; NISQ Devices; Semantic Representation; Quantum Embedding
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-04
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.69623
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E-ISSN 2582-2160
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