QESAN: A Quantum-Enhanced Semantic Attention Network for Accurate Text Classification
S.Amsaveni, P. Lalithamani, S. Punithadevi +1 more
Text classification remains among the most significant tasks in natural language processing and the field has been used in healthcare, law, social media analysis, and economic literature. Classical attention networks existing machine learning approaches and deep learning approaches, including LSTM, BiLSTM, Transformers, and classical attention networks, are susceptible to such problems as long-range dependencies and semantic ambiguity, as well as sparse or underrepresented text. To deal with these deficiencies, this research presents a proposed a Quantum-Enhanced Semantic Attention Network (QESAN), a quantum computing concept integrated into semantic attention-based deep learning paradigm. The proposed QESAN coded textual features using amplitude encoding, quantum superposition, and entanglement-like transformations, such that the model is able to learn not only complex relations between interdependencies, but also fairly subtle semantic relations which are generally hard to learn on classical models. The result of these quantum-enhanced representations is then sent through an attention mechanism which acts on the most semantically significant aspects to produce results that are context-sensitive and can be interpreted. The Kaggle Quantum Programming experimental results indicate that QESAN is effective with accuracy of 98.7 which is far superior than the LSTM, BiLSTM, Transformer, and classical attention networks. The results indicate the ability of QESAN to solve the conventional issues of the text classification process particularly in the assignment of dealing with the long-range dependencies, semantic ambiguity, and sparse data. Overall, the integration of the concept of quantum computing with the attention-based deep learning provides the scalable and resilient solution to the issue of the modern text classification problems of the high-performance.
International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN)•Nov 24, 2025•1 citation