Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866912217218154496 |
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| author | Du, Yuxuan Wang, Xinbiao Guo, Naixu Yu, Zhan Qian, Yang Zhang, Kaining Hsieh, Min-Hsiu Rebentrost, Patrick Tao, Dacheng |
| author_facet | Du, Yuxuan Wang, Xinbiao Guo, Naixu Yu, Zhan Qian, Yang Zhang, Kaining Hsieh, Min-Hsiu Rebentrost, Patrick Tao, Dacheng |
| contents | This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity. In addition, practical code demonstrations are provided in https://qml-tutorial.github.io/ to illustrate real-world implementations and facilitate hands-on learning. Together, these elements offer readers a comprehensive overview of the latest advancements in QML. By bridging the gap between classical machine learning and quantum computing, this tutorial serves as a valuable resource for those looking to engage with QML and explore the forefront of AI in the quantum era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01146 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers Du, Yuxuan Wang, Xinbiao Guo, Naixu Yu, Zhan Qian, Yang Zhang, Kaining Hsieh, Min-Hsiu Rebentrost, Patrick Tao, Dacheng Quantum Physics Artificial Intelligence Machine Learning This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity. In addition, practical code demonstrations are provided in https://qml-tutorial.github.io/ to illustrate real-world implementations and facilitate hands-on learning. Together, these elements offer readers a comprehensive overview of the latest advancements in QML. By bridging the gap between classical machine learning and quantum computing, this tutorial serves as a valuable resource for those looking to engage with QML and explore the forefront of AI in the quantum era. |
| title | Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.01146 |