Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo, Zhan Yu, Yang Qian, Kaining Zhang,, Min-Hsiu Hsieh, Patrick Rebentrost, Dacheng Tao

TL;DR
This tutorial provides an accessible introduction to quantum machine learning, covering foundational principles, algorithms, applications, and practical code demonstrations for AI practitioners and researchers.
Contribution
It offers a comprehensive, hands-on overview of QML, bridging classical AI and quantum computing with practical examples and recent advancements.
Findings
Introduction to key QML algorithms and principles
Practical code demonstrations for real-world applications
Discussion of challenges like trainability and complexity
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture
