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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15287 |
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| _version_ | 1866916372984889344 |
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| author | Zhou, Yifan Xu, Chong Cheng Song, Mingi Wong, Yew Kee |
| author_facet | Zhou, Yifan Xu, Chong Cheng Song, Mingi Wong, Yew Kee |
| contents | This paper explores the transformative potential of quantum computing in the realm of personalized learning. Traditional machine learning models and GPU-based approaches have long been utilized to tailor educational experiences to individual student needs. However, these methods face significant challenges in terms of scalability, computational efficiency, and real-time adaptation to the dynamic nature of educational data. This study proposes leveraging quantum computing to address these limitations. We review existing personalized learning systems, classical machine learning methods, and emerging quantum computing applications in education. We then outline a protocol for data collection, privacy preservation using quantum techniques, and preprocessing, followed by the development and implementation of quantum algorithms specifically designed for personalized learning. Our findings indicate that quantum algorithms offer substantial improvements in efficiency, scalability, and personalization quality compared to classical methods. This paper discusses the implications of integrating quantum computing into educational systems, highlighting the potential for enhanced teaching methodologies, curriculum design, and overall student experiences. We conclude by summarizing the advantages of quantum computing in education and suggesting future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum-Powered Personalized Learning Zhou, Yifan Xu, Chong Cheng Song, Mingi Wong, Yew Kee Quantum Physics Machine Learning This paper explores the transformative potential of quantum computing in the realm of personalized learning. Traditional machine learning models and GPU-based approaches have long been utilized to tailor educational experiences to individual student needs. However, these methods face significant challenges in terms of scalability, computational efficiency, and real-time adaptation to the dynamic nature of educational data. This study proposes leveraging quantum computing to address these limitations. We review existing personalized learning systems, classical machine learning methods, and emerging quantum computing applications in education. We then outline a protocol for data collection, privacy preservation using quantum techniques, and preprocessing, followed by the development and implementation of quantum algorithms specifically designed for personalized learning. Our findings indicate that quantum algorithms offer substantial improvements in efficiency, scalability, and personalization quality compared to classical methods. This paper discusses the implications of integrating quantum computing into educational systems, highlighting the potential for enhanced teaching methodologies, curriculum design, and overall student experiences. We conclude by summarizing the advantages of quantum computing in education and suggesting future research directions. |
| title | Quantum-Powered Personalized Learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2408.15287 |