Shahir, Rafiad SadatTomal, S.M. Yousuf IqbalShafin, Abdullah AlBhattacharjee, DebojitAmin, MD. Khairul2026-01-192026-01-1920252025-06ID 21301129ID 21201631ID 20201159ID 21201167http://hdl.handle.net/10361/27456Cataloged from PDF version of thesis.Includes bibliographical references (pages 40-41).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Recent advances in quantum computing have opened new pathways for enhancing deep learning architectures, particularly in domains characterized by high-dimensional and context-rich data such as natural language processing (NLP). In this work, we present a hybrid classical–quantum Transformer model that integrates a quantum-enhanced attention mechanism into the standard classical architecture. By embedding token representations into a quantum Hilbert space via parameterized variational circuits and exploiting entanglement-aware kernel similarities, the model captures complex semantic relationships beyond the reach of conventional dot-product attention. We demonstrate the effectiveness of this approach across diverse NLP benchmarks, showing improvements in both efficiency and representational capacity. Empirical study reveals that the quantum attention layer yields globally coherent attention maps and more separable latent features, while requiring comparatively fewer parameters than classical counterparts. These findings highlight the potential of quantum-classical hybrid models to serve as a powerful and resource-efficient alternative to existing attention mechanisms in NLP.42 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Natural language processingQuantum attentionDeep learningVariational quantum circuitVQCHybrid quantum-classical modelQuantum kernel methodNatural language processing (Computer science).Quantum computing.Deep learning (Machine learning).Thesis