Integrating quantum CI and generative AI for Taiwanese/English co-learning

Lee, C-S, Wang, M-H, Chen, C-Y, Yang, S-C, Reformat, M, Kubota, N and Pourabdollah, A ORCID: https://orcid.org/0000-0001-7737-1393, 2024. Integrating quantum CI and generative AI for Taiwanese/English co-learning. Quantum Machine Intelligence, 6: 64. ISSN 2524-4906

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Abstract

This paper proposes a quantum computational intelligence (QCI) model integrated with generative artificial intelligence (GAI) for Taiwanese/English language co-learning applications within human–machine interactions, focusing on Trustworthy AI Dialogue Engine (TAIDE)-based knowledge graph construction and multimodal data transformation. The QCI model comprises two main phases: human–machine interaction and data processing for quantum circuit generation and real-world applications. During the human–machine interaction phase, a synergy between human intelligence (HI) and machine intelligence (MI) enables young students to gain familiarity with CI that converges with QCI. The second phase involves data processing, which encompasses stages of data preprocessing, analysis, and evaluation. The methodology is applied to two distinct applications: Application 1 focuses on constructing a knowledge graph using the Ollama platform and the TAIDE model developed by the Taiwanese government based on the LLaMa 2 model. Application 2 addresses the GAI images to text/voice and text/voice to GAI images, depending on the type of Taiwanese/English data collected. Subsequently, the QCI model is refined through particle swarm optimization (PSO) and genetic algorithm neural networks (GANN). Moreover, a quantum fuzzy inference mechanism (QFIM) is integrated into the developed QCI&AI-FML learning platform to generate quantum circuits for the QCI model, which helps teach young students and facilitate their learning of QCI. The experimental results indicate that the QCI model significantly enhances human–machine collaboration. Looking forward, we plan to extend the QCI model to reach more young learners.

Item Type:	Journal article
Publication Title:	Quantum Machine Intelligence
Creators:	Lee, C.-S., Wang, M.-H., Chen, C.-Y., Yang, S.-C., Reformat, M., Kubota, N. and Pourabdollah, A.
Publisher:	Springer Science and Business Media LLC
Date:	2024
Volume:	6
ISSN:	2524-4906
Identifiers:	Number Type 10.1007/s42484-024-00195-8 DOI 2227037 Other
Rights:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s42484-024-00195-8.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	27 Sep 2024 09:01
Last Modified:	27 Sep 2024 09:01
URI:	https://irep.ntu.ac.uk/id/eprint/52311

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