Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation

Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation Chang-Shing Lee, Mei-Hui Wang, Chih-Yu Chen, Sheng-Chi Yang, Marek Reformat, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4169544/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Sep, 2024 Read the published version in Quantum Machine Intelligence → Version 1 posted 9 You are reading this latest preprint version 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. 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: 1) Application 1 focuses on constructing a knowledge graph using the Ollama platform and the TAIDE model—a Trustworthy AI Dialogue Engine developed by the Taiwanese government based on the LLaMa 2 model. 2) 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 to enhance the QCI model’s capability in creating a quantum circuit. The experimental results suggest that the QCI model significantly enhances human-machine collaboration. Looking forward, we plan to extend the QCI model to reach more young learners. Human-Machine Interaction Quantum Computational Intelligence Generative AI Particle Swarm Optimization Genetic Algorithm Neural Network TAIDE Knowledge Graph Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Sep, 2024 Read the published version in Quantum Machine Intelligence → Version 1 posted Editorial decision: Revision requested 28 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor assigned by journal 11 Apr, 2024 Submission checks completed at journal 26 Mar, 2024 First submitted to journal 26 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4169544","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284105501,"identity":"02004b7d-c93f-4420-9173-385be04fa0ae","order_by":0,"name":"Chang-Shing 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