A Hybrid Quantum-Classical Attention Mechanism for Efficient Large Language Models
By Smaran, Ansh
Our proposed research aims to explore new methods that improve efficiency in large language models by designing a hybrid quantum-classical attention mechanism. The attention layer acts as the mechanism that dynamically weights and highlights relevant parts while generating each output token. Self-attention, a core component, enables these models to capture long-range relations yet creates quadratic computational cost [1]. This project will test whether a quantum module inside the attention mechanism reduces this bottleneck while preserving accuracy and stability. By testing feasibility, our research seeks to identify practical applications that integrate quantum computation and modern natural language processing systems.
Intellectual Merit The intellectual merit of this project lies in advancing efficient architectures for large language models (LLMs) by exploring a hybrid quantum-classical attention mechanism. While LLMs achieved remarkable success in translation, dialogue, and reasoning, reliance on quadratic-cost self-attention creates severe efficiency bottlenecks. Recent quantum self-attention approaches, such as QSANN and QMSAN, show the potential of quantum circuits to compute similarity across exponentially large Hilbert spaces [2–4]. However, they remain limited by noise and low qubit counts in near-term devices. By offloading the query-key similarity step, which stands as the most computationally intensive, to quantum modules while preserving classical value and residual pathways, this project aims to combine the power of quantum systems with the scalability and robustness of classical deep learning.
Broader Impact The potential broader impacts of our work extend to the computing community and other fields that rely on language models. By improving efficiency in attention mechanisms, this research reduces environmental and economic costs linked to training and deploying LLMs, a major concern as model sizes continue to grow [5]. The hybrid approach also creates new opportunities for specialized domain-specific LLMs. For example, these models could operate under constrained settings such as onboard medical devices, educational platforms, and edge computing. More generally, developing methods that merge quantum and classical computing builds the foundation for future high-performance AI systems.