Preface, xiiiPart I Artificial Intelligence, 11 Introduction, 31.1 Motivation, 31.2 Book Structure, 52 Machine Learning Algorithms, 172.1 Fundamentals, 172.2 ML Algorithm Analysis, 373 Artificial Neural Networks, 553.1 Multi-layer Feedforward Neural Networks, 553.2 FIR Architecture, 603.3 Time Series Prediction, 683.4 Recurrent Neural Networks, 693.5 Cellular Neural Networks (CeNN), 813.6 Convolutional Neural Network (CoNN), 844 Explainable Neural Networks, 974.1 Explainability Methods, 994.2 Relevance Propagation in ANN, 1034.3 Rule Extraction from LSTM Networks, 1104.4 Accuracy and Interpretability, 1125 Graph Neural Networks, 1355.1 Concept of Graph Neural Network (GNN), 1355.2 Categorization and Modeling of GNN, 1445.3 Complexity of NN, 1566 Learning Equilibria and Games, 1796.1 Learning in Games, 1796.2 Online Learning of Nash Equilibria in Congestion Games, 1966.3 Minority Games, 2026.4 Nash Q-Learning, 2046.5 Routing Games, 2116.6 Routing with Edge Priorities, 2207 AI Algorithms in Networks, 2277.1 Review of AI-Based Algorithms in Networks, 2277.2 ML for Caching in Small Cell Networks, 2377.3 Q-Learning-Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks, 2437.4 ML for Self-Organizing Cellular Networks, 2527.5 RL-Based Caching, 2677.6 Big Data Analytics in Wireless Networks, 2747.7 Graph Neural Networks, 2797.8 DRL for Multioperator Network Slicing, 2917.9 Deep Q-Learning for Latency-Limited Network Virtualization, 3027.10 Multi-Armed Bandit Estimator (MBE), 3177.11 Network Representation Learning, 327Part II Quantum Computing, 3618 Fundamentals of Quantum Communications, 3638.1 Introduction, 3638.2 Quantum Gates and Quantum Computing, 3728.3 Quantum Fourier Transform (QFT), 3869 Quantum Channel Information Theory, 3979.1 Communication Over a Channel, 3989.2 Quantum Information Theory, 4019.3 Channel Description, 4079.4 Channel Classical Capacities, 4149.5 Channel Quantum Capacity, 4319.6 Quantum Channel Examples, 43710 Quantum Error Correction, 45110.1 Stabilizer Codes, 45810.2 Surface Code, 46510.3 Fault-Tolerant Gates, 47110.4 Theoretical Framework, 47411 Quantum Search Algorithms, 49911.1 Quantum Search Algorithms, 49911.2 Physics of Quantum Algorithms, 51012 Quantum Machine Learning, 54312.1 QML Algorithms, 54312.2 QNN Preliminaries, 54712.3 Quantum Classifiers with ML: Near-Term Solutions, 55012.4 Gradients of Parameterized Quantum Gates, 56012.5 Classification with QNNs, 56812.6 Quantum Decision Tree Classifier, 57513 QC Optimization, 59313.1 Hybrid Quantum-Classical Optimization Algorithms, 59313.2 Convex Optimization in Quantum Information Theory, 60113.3 Quantum Algorithms for Combinatorial Optimization Problems, 60913.4 QC for Linear Systems of Equations, 61413.5 Quantum Circuit, 62513.6 Quantum Algorithm for Systems of Nonlinear Differential Equations, 62814 Quantum Decision Theory, 63714.1 Potential Enablers for Qc, 63714.2 Quantum Game Theory (QGT), 64114.3 Quantum Decision Theory (QDT), 66514.4 Predictions in QDT, 67615 Quantum Computing in Wireless Networks, 69315.1 Quantum Satellite Networks, 69315.2 QC Routing for Social Overlay Networks, 70615.3 QKD Networks, 71316 Quantum Network on Graph, 73316.1 Optimal Routing in Quantum Networks, 73316.2 Quantum Network on Symmetric Graph, 74416.3 QWs, 74716.4 Multidimensional QWs, 75317 Quantum Internet, 77317.1 System Model, 77517.2 Quantum Network Protocol Stack, 789References, 814Index, 821
Savo G. Glisic is Research Professor at Worcester Polytechnic Institute, Massachusetts, USA. His research interests include network optimization theory, network topology control and graph theory, cognitive networks, game theory, artificial intelligence, and quantum computing technology.Beatriz Lorenzo is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, USA. Her research interests include the areas of communication networks, wireless networks, and mobile computing.