手把手教AI从业者入门量子机器学习，打通经典与量子的隔阂。
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
- 从基础原理到典型算法，系统梳理量子机器学习核心方法。
- 结合可运行代码，展示真实场景中的量子机器学习实现。
- 适合想切入量子人工智能前沿的研究者和工程师阅读。
本教程面向具备人工智能背景的读者，介绍量子机器学习（QML）这一快速发展的领域——旨在利用量子计算机的能力重塑机器学习格局。为确保自洽性，教程涵盖基础原理、代表性QML算法、潜在应用场景，以及可训练性、泛化能力与计算复杂性等关键问题。此外，通过 https://qml-tutorial.github.io/ 提供实践代码演示，展示真实世界中的实施案例，促进动手学习。这些内容共同为读者提供量子机器学习最新进展的全面概览。通过弥合经典机器学习与量子计算之间的鸿沟，本教程成为希望参与该领域、探索量子时代AI前沿的研究者和实践者的宝贵资源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。