Quantum Machine Learning: Theory and Practice

This book primarily introduces the background knowledge and fundamental concepts of quantum machine learning, as well as the basic principles and implementation of several important quantum machine learning algorithms.

It is structured into nine chapters, covering the following main topics: background knowledge of quantum machine learning, fundamentals of quantum computing, the quantum machine learning framework VQNet, support vector machines, clustering, convolutional neural networks, recurrent neural networks, generative adversarial networks, and natural language processing.

This book can serve as a reference for graduate students, teachers, and researchers in relevant fields at universities or research institutes. It is also suitable as a self-study guide for quantum machine learning enthusiasts.