量子机器学习中数据挖掘的量子计算方法（英文版）

Machine learning is a dynamic field that offers solutions for tasks ranging from identifying patterns in text collections to analyzing live sensor data. Quantum information theory provides a new realm of possibilities for computing faster and more efficiently than classical methods. In their book, Quantum Machine Learning: Data Mining with Quantum Computing, the authors explore the intersection of these two fields, introducing readers to cutting-edge algorithms for clustering, pattern recognition, and regression analysis enhanced by quantum computing techniques. The book is divided into three parts, beginning with fundamental concepts in machine learning, quantum mechanics, computing, and information theory. Part two dives deeper into classical learning algorithms such as unsupervised and supervised learning, neural networks, and regression analysis. Finally, the authors introduce quantum clustering, pattern recognition, classification, and boosting algorithms in part three. The authors take a clear and concise approach to explaining complex concepts, making this book accessible to readers with a background in either quantum computing or machine learning. The book's comprehensive coverage and up-to-date insights make it a valuable resource for academics, researchers, and professionals in computer science, data analytics, or physics. Overall, Quantum Machine Learning: Data Mining with Quantum Computing is a must-read for anyone who wants to stay ahead of the curve in the rapidly evolving fields of machine learning and quantum computing. Its focus on practical applications and accessible language make it a useful guide for anyone looking to integrate quantum techniques into their data analysis toolkit.