Language Model Optimization Based on Quantum Machine Learning for Long Chinese Text Processing

Chinese language presents unique challenges in text processing due to its complex structure, including high inter-lexical dependence, flexible word order, and rich contextual expressions. These characteristics make it particularly difficult for large models to effectively handle long Chinese texts using traditional machine learning (ML) algorithms. This paper explores the application of Quantum Machine Learning (QML) as a solution to enhance the performance of large language models (LLMs) in processing long Chinese texts. By leveraging the inherent advantages of QML, such as higher processing efficiency and deeper semantic understanding, this research demonstrates through experimental comparisons that QML models significantly outperform traditional ML models in both speed and comprehension accuracy. The paper systematically introduces text processing methods using both traditional ML and QML, and presents a simplified QML model designed to enhance text processing capabilities. The findings underscore the potential of QML in optimizing the future performance of LLMs, particularly for languages with complex linguistic structures like Chinese. This research holds significant implications for advancing LLM technologies in text-heavy applications.