Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approach

Central to these advancements are transformerbased architectures, including BERT and GPT, which employ self-attention mechanisms to model long-range dependencies in text, achieving superior performance compared to traditional recurrent models [1, 2]. However, the success of these architectures comes at the cost of high computational complexity, requiring substantial memory and processing power to handle increasing dataset sizes and model intricacies. While transformers have set state-of-the-art benchmarks, their resource demands make them unsuitable for real-time applications or deployment in resource-constrained environments. Concurrently, quantum computing has emerged as a disruptive paradigm, introducing principles like superposition and entanglement, which enable quantum systems to process complex computations in ways unattainable by classical systems [3, 5]. Despite its potential, quantum computing faces challenges such as limited qubit counts, high error rates, and difficulties in scaling to larger datasets [4, 6]. These limitations necessitate hybrid approaches that integrate quantum and classical systems to harness the best of both worlds. This paper addresses the computational bottlenecks of transformers by proposing a hybrid quantum-classical model.