Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers

• URL: http://arxiv.org/abs/2502.01146v1
• Date: Mon, 03 Feb 2025 08:33:44 GMT
• Title: Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
• Authors: Yuxuan Du, Xinbiao Wang, Naixu Guo, Zhan Yu, Yang Qian, Kaining Zhang, Min-Hsiu Hsieh, Patrick Rebentrost, Dacheng Tao,
• Abstract summary: This tutorial introduces readers with a background in AI to quantum machine learning (QML) For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity.
• Score: 51.03113410951073
• License:
• Abstract: 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.

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