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Multilingual multi-task quantum transfer learning
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AI
DOI：10.1007/s42484-025-00260-w.png)
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Abstract
En 中文
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning-using quantum circuits for fine-tuning pre-trained classical models specific to a task-is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks-semantic and syntactic-and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.
Keywords:
Quantum machine learning
Quantum natural language processing
Variational quantum classifier
Natural language processing
Neural language models
Journal
Q
IF:
4.4
Papers:
436
Citations:
796