Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
It provides an incremental resource for machine learning practitioners and researchers to engage with QML, addressing the gap between classical and quantum approaches.
This tutorial introduces quantum machine learning (QML) to AI practitioners, covering foundational principles, algorithms, and practical implementations to bridge classical machine learning and quantum computing.
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.