Artificial intelligence (AI) and quantum computing are widely discussed topics in the computing field. So it is no surprise that this book is about AI and quantum computing for advanced wireless networks. The book is divided into two parts: one on AI and the other on quantum computing.

The first part, on AI, consists of seven chapters. The brief introductory chapter discusses the structure of the book. It provides the motivation and includes helpful references. The second chapter is on machine learning algorithms. Many topics are talked about, including regression, decision trees, bagging, boosting, support vector machines, naive Bayes, *k*-nearest neighbors *k*NN, *k*-means, dimensionality reduction, and analysis of machine learning algorithms. The third chapter is on artificial neural networks (ANNs); concepts such as multi-layer feedforward neural networks, recurrent neural networks (RNNs), and cellular and convolutional neural networks are emphasized. Chapters 4 through 7 are, respectively, “Explainable Neural Networks,” “Graph Neural Networks,” “Learning Equilibria and Games,” and “AI Algorithms in Networks.”

Part 2’s nine chapters (8 through 17) are all about quantum computing, including theory, error correction, search algorithms, machine learning, optimization, and so on.

The book is well organized. There are numerous illustrations, many in color. Its coverage of key concepts is good. The chapters end with numerous facilitative references to the literature. There is a handy index. The book offers an all-encompassing demonstration of the effectuation of AI and quantum computing in large communication networks. One of the authors, Savo Glisic, has authored several books on related topics. This book will be useful for academics, network engineers, policymakers, practitioners, researchers, regulators, and students. In general, this timely and accessible book will be useful for anyone interested in knowing about applications of AI and quantum computing for advanced wireless networks.