A Comprehensive Review of Hybrid Quantum–Classical Frameworks for Scalable Text Classification and Contextual Embedding Generation in Natural Language Processing

Natural Language Processing (NLP) has experienced significant advancements through the development of classical machine learning, deep learning, graph-based, and transformer architectures for tasks such as text classification, semantic analysis, and contextual embedding generation. However, the growing complexity of textual data creates challenges related to scalability, computational efficiency, contextual understanding, and resource consumption. Recently, hybrid quantum–classical frameworks have emerged as promising solutions by leveraging quantum parallelism, entanglement, and high-dimensional feature representation capabilities. This review paper presents a comprehensive comparative analysis of various NLP approaches, including SVM, Random Forest, Naïve Bayes, LSTM, GCN, Quantum State Embedding Attention (QSEA), Quantum-based LSTM (QLSTM), Vision Transformer-integrated hybrid pipelines, and HQML-NLP frameworks. The study evaluates these models based on methodology, strengths, limitations, and NLP applications while identifying key research gaps such as scalability limitations, hardware constraints, benchmarking challenges, and explainability issues in quantum NLP systems. Furthermore, the paper formulates research hypotheses and highlights future research directions for the development of scalable, efficient, and context-aware hybrid quantum NLP architectures.