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Quantum Information and Computation

Analyzing Grammatical Rules and Lexical Evolution in Chinese Language Literature Texts Based on Quantum Computing Models

Sha Liu, Wenshu Deng

Published Jun 1, 2026• 0 citations
Publisher

Abstract

Abstract This paper systematically explores the semantic computation methods in Chinese language and literature texts based on the quantum computation model. A quantum computing framework adapted to Chinese language analysis is constructed, including quantum state representation, semantic representation and interference term derivation. The analysis effect of the proposed quantum computing model is verified through typical tasks in Chinese language literature such as sentiment classification and word sense disambiguation. It is confirmed that in the task of sentiment categorization, this paper’s model achieves the best prediction results on all four datasets, with accuracies AR (95.32%), MPQA (91.44%), LR (91.04%), and SST-2 (93.57%), which are higher than the next best performer, ERNIE, by 4.17%, 2.07%, 1.92%, and 3.28%, respectively. In the word sense disambiguation task, the model in this paper still achieves optimal results. In the text categorization task, the average F1 value is 89.35% in 10 categories, among which the best effect is achieved for text categorization in the category of poetry, with an F1 value of 93.14%. The quantum computing model provides a new idea for Chinese language text analysis, which can capture semantic associations and lexical changes in the text more effectively.

DOI
10.2478/qic-2026-0029

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Abstract This paper systematically explores the semantic computation methods in Chinese language and literature texts based on the quantum computation model. A quantum computing framework adapted to Chinese language analysis is constructed, including quantum state representation, semantic representation and interference term derivation. The analysis effect of the proposed quantum computing model is verified through typical tasks in Chinese language literature such as sentiment classification and word sense disambiguation. It is confirmed that in the task of sentiment categorization, this paper’s model achieves the best prediction results on all four datasets, with accuracies AR (95.32%), MPQA (91.44%), LR (91.04%), and SST-2 (93.57%), which are higher than the next best performer, ERNIE, by 4.17%, 2.07%, 1.92%, and 3.28%, respectively. In the word sense disambiguation task, the model in this paper still achieves optimal results. In the text categorization task, the average F1 value is 89.35% in 10 categories, among which the best effect is achieved for text categorization in the category of poetry, with an F1 value of 93.14%. The quantum computing model provides a new idea for Chinese language text analysis, which can capture semantic associations and lexical changes in the text more effectively.
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.
In this work, we conduct a sentiment analysis of English-language reviews using a quantum-like (wave-based) model of text representation. This model is explored as an alternative to machine learning (ML) techniques for text classification and analysis tasks. Special attention is given to the problem of segmenting text into semantic units, and we illustrate how the choice of segmentation algorithm is influenced by the structure of the language. We investigate the impact of quantum-like semantic interference on classification accuracy and compare the results with those obtained using classical probabilistic methods. Our findings show that accounting for interference effects improves accuracy by approximately 15%. We also explore methods for reducing the computational cost of algorithms based on the wave model of text representation. The results demonstrate that the quantum-like model can serve as a viable alternative or complement to traditional ML approaches. The model achieves classification precision and recall scores of around 0.8. Furthermore, the classification algorithm is readily amenable to optimization: the proposed procedure reduces the estimated computational complexity from O(n2) to O(n).
In recent years Quantum computers have been widely used for image manipulation. Similarly, NLP helps understand text and spoken language as humans do. The application of quantum computing to Natural Language Processing has yielded a new field of research, which is known as quantum natural language processing. This paper focuses on listing all the NLP approaches and categorize them based on theoretical work and those implemented on classical or quantum hardware; and also based on task, i.e., general purpose syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering,and finally by the resources used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used.
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Quantum computing has emerged as a breakthrough technology for sentiment and emotion analysis, providing computational efficiency and interpretability benefits. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing models. The research uses six benchmark sentiment datasets (CR, MPQA, MR, SST-2, SUBJ, and SST-5) to compare the performance of the methods. The approach involves representing textual and multimodal data as complex quantum states, using quantum-inspired feature representations, and using quantum measurement methods for classification. The results show that the Complex Quantum Neural Network (QNN) performs better than baseline models such as BERT and RoBERTa, with the best F1-scores on all datasets. Specifically, it has an F1-score of 86 on CR, 86 on MPQA, 90 on MR, 88 on SST-2, 97 on SUBJ, and 53 on SST-5, outperforming baseline deep learning methods. These results show the potential of quantum computing to revolutionize sentiment analysis, providing more efficient and interpretable models with better contextual understanding and scalability.