Publications and Research
Document Type
Article
Publication Date
5-30-2025
Abstract
The training of large language models (LLMs) presents significant computational challenges, particularly regarding efficient convergence. This paper presents a hybrid quantum-classical framework designed to address the significant computational challenges associated with training large language models (LLMs). By integrating quantum computing principles superposition, entanglement, and tunneling with classical deep learning methods, we propose an approach to accelerate convergence, enhance optimization efficiency, and improve model generalization. Specifically, quantum feature mapping is employed to project classical data into high-dimensional Hilbert spaces, facilitating more expressive data representations. Quantum-assisted optimization algorithms, such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), efficiently navigate non-convex loss landscapes, mitigating issues of local minima encountered by classical methods. Furthermore, quantum-accelerated matrix operations, leveraging techniques like the Harrow-Hassidim-Lloyd (HHL) algorithm and Quantum Fourier Transform (QFT), offer computational speed-ups essential for LLM training. Quantum measurement introduces natural stochasticity, serving as a regularization mechanism analogous to classical dropout, thereby enhancing robustness and generalization. Despite current quantum hardware constraints characteristic of the Noisy Intermediate-Scale Quantum (NISQ) era including decoherence, gate noise, and limited qubit connectivity our theoretical framework and conceptual analyses demonstrate the feasibility and potential advantages of hybrid quantum-classical methodologies. This work lays a foundation for future research, aiming toward practical implementations as quantum hardware continues to mature.
Comments
This is the author's accepted manuscript of an article originally published in Proceedings Volume 13451, Quantum Information Science, Sensing, and Computation XVII, available at https://doi.org/10.1117/12.3057017