Preface PART 1:ARTIFICIAL INTELLIGENCE Ch 1. INTRODUCTION 1.1 Motivation 1.2 Book Structure Ch 2 ML ALGORITHMS 2.1. Fundamentals 2.1.1.

Linear Regression 2.1.2 Logistic Regression 2.1.3 Decision Tree: Regression Trees vs Classification Trees 2.1.4 Trees in R and Python 2.1.

5 Bagging and Random Forest 2.1.6 Boosting GBM and XGboost 2.1.7. SVM Support Vector Machine 2.1.8 Naive Bayes , kNN, K Means 2.

1.9 Dimensionality Reduction 2.2. ML Algorithms Analysis 2.2.1 Logistic Regression 2.2.2.

Decision Trees ClassiFiers 2.2.3 Dimensionality reduction techniques 2 REFERENCES Ch 3 ARTIFICIAL NEURAL NETWORKS 3.1 Multi-layer Feedforward Neural Networks 3.1.1 Single Neurons 3.1.2 Weights Optimization 3.

2 FIR Architecture 3.2.1 Spatial Temporal Representations 3.2.2. Neural Network Unfolding 3.2.3 Adaptation 3.

3 Time Series Prediction 3.3.1 Adaptation and Iterated Predictions 3.4. Recurrent Neural Networks 3.4.1 Filters as Predictors 3.4.

2 Feedback Options in Recurrent Neural Networks 3.4.3 Advanced RNN Architectures 3.5 Cellular Neural Networks (CeNN) 3.6 Convolutional CoNN 3.6.1 CoNN Architecture 3.6.

2 Layers in CoNN 3 REFERENCES Ch 4 EXPLAINABLE NN 4.1 Explainability Methods 4.1.1 The complexity and Interoperability 4.1.2 Global Versus Local Interpretabity 4.1.3 Model Extraction 4.

2 Relevance Propagation in ANN 4.2.1 Pixel-Wise Decomposition 4.2.2 Pixel-Wise Decomposition for Multilayer NN 4.3 Rule Extraction from LSTM Networks 4.4 Accuracy and Interpretability 4.4.

1 Fuzzy Models 4.4.2 Support Vector Regression 4.4.3 Combination of Fuzzy Models and SVR 4 REFERENCES Ch 5 GRAPH NEURAL NETWORKS 5.1 Concept of graph neural network (GNN) 5.1.1 Classification of Graphs 5.

1.2 Propagation Types 5.1.3 Graph Networks 5.2 Categorization and Modeling of GNN 5.2.1 Recurrent Graph Neural Networks (RecGNNs) 5.2.

2 Convolutional Graph Neural Networks (ConvGNNs) 5.2.3 Graph Autoencoders (GAEs) 5.2.4 SpatialTemporal Graph Neural Networks (STGNNs) 5.3 Complexity of NN 5.3.1 Labeled Graph NN (LGNN) 5.

3.2 Computational Complexity Appendix 5.1 Appendix 5.2 Graph Fourier Transform Ch 6 LEARNING EQUILIBRIA AND GAMES 6.1 Learning in Games 6.1.1 Learning Equilibria of Games 6.2 Online Learning of Nash Equilibria in Congestion Games 6.

3 Minority Games 6.4 Nash Q-Learning 6.4.1 Multiagent Qlearning 6.4.2 Convergence 6.5 Routing Games 6.5.

1 Nonatomic Selfish Routing 6.5.2 Atomic Selfish Routing 6.5.3 Existence of Equilibrium 6.5.4 Reducing the Price of Anarchy 6.6.

Routing with Edge Priorities 6.6.1 Computing Equilibria 6 REFERENCES Ch 7 AI ALGORITHMS IN NETWORKS 7.1. AI Based Algorithms in Networks 7.1.2 Traffic classification 7.1.

3 Traffic Routing 7.1.4 Congestion Control 7.1.5 Resource Management 7.1.6 Fault management 7.1.

7 QoS and QoE management 7.1.8 Network security 7.2 ML for Caching in Small Cell Networks 7.2.1 System Model 7.3 Q-learning Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks 7.3.

1 Stochastic NonCooperative Game 7.3.2 Multi-Agent Q-Learning 7.3.3 Q-learning for Channel and Power Level Selection 7.4 ML for Self-Organizing Cellular Networks 7.4.1 Learning In Self-Configuration 7.

4.2 RL for SON Coordination 7.4.2a SON Function Model 7.4.2b Reinforcement Learning 7.5 RL Based Caching 7.5.

1 System Model 7.5.2 Optimality Conditions 7.6 Big Data Analytics in Wireless Networks 7.6.1. Evolution of Analytics 7.6.

2 Data-Driven Networks Optimization 7.7. Graph Neural Networks for 7.7.1 Network Virtualization 7.7.2 GNNBased Dynamic Resource Management 7.8 DRL for Multioperator Network Slicing 7.

8.1 System Model 7.8.2 System Optimization 7.8.3 Game Equilibria by DRL 7.9 Deep Q-Learning for Latency Limited Network Virtualization 7.9.

1. System Model 7.9.2 Learning and Prediction 7.9.3 DRL for Dynamic VNF Migration 7.10 Multiarmed Bandit Estimator MBE 7.10.

1 System Model 7.10.2 System Performance 7.11 Network Representation Learning 7.11.1Network properties 7.11.2 Unsupervised NRL 7.

11.3 SemiSupervised NRL 7 REFERENCES PART 2:QUANTUM COMPUTING Ch8 FUNDAMENTALS OF QUANTUM COMMUNICATIONS 8.1 Introduction 8.2. Quantum Gates and Quantum Computing 8.2.1 Quantum circuits 8.2.

2 Quantum algorithms 8.3 Quantum Fourier Transform 8.3.1 QFT vs FFT Revisited 8 REFERENCE Ch 9 QUANTUM CHANNEL INFORMATION THEORY 9.1 Communication Over a Q Channel 9.1 Quantum Information Theory 9.1.1 Density Matrix and Trace Operator 9.

1.2 Quantum Measurement 9.2 Q Channel Description 9.2.1 Q Channel Entropy 9.2.2 A Bit on History 9.3 Q Channel Classical Capacities 9.

3.1 Capacity of Classical Channels 9.3.2 The Private Classical Capacity 9.3.3 The EntanglementAssisted Classical Capacity 9.3.4 The Classical ZeroError Capacity 9.

3.5 EntanglementAssisted Classical ZeroError Capacity 9.4 Q Channel Quantum Capacity 9.4.1 Preserving Quantum Information 9.4.2 Quantum Coherent Information 9.4.

3 Connection Between Classical and Quantum Information 9.5 Quantum Channel Examples 9.5.1. Channel Maps 9.5.2. Capacities 9.

5.3 Q Channel Parameters 9 REFERENCES Ch 10 QUANTUM ERROR CORRECTION 10.1 Stabilizer codes 10.2 Surface Code 10.2.1 The rotated lattice 10.3 Fault-tolerant gates 10.3.

1 Fault Tolerance 10.4 Theoretical Framework 10.4.1 Classical error correction 10.4.2. Theory of Quantum Error Correction Appendix: Binary fields and discrete vector spaces Appendix 1: A Bit on Noise Physics 10 REFERENCES Ch 11 QSA ALGORITHMS 11.1 Quantum Search Algorithms 11.

1.1 The Deutsch Algorithm 11.1.2 The Deutsch-Jozsa Algorithm 11.1.3 Simon''s Algorithm 11.1.4 Shor''s Algorithm 11.

1.5 Quantum Phase Estimation Algorithm 11.1.6 Grover''s Quantum Search Algorithm 11.1.7 Boyer-Brassard-Høyer-Tapp Quantum Search Algorithm 11.1.8 Dürr-Høyer Quantum Search Algorithm 11.

1.9 Quantum Counting Algorithm 11.1.10 Quantum Heuristic Algorithm 11.1.11 Quantum Genetic Algorithm 11.1.12 Harrow-Hassidim-Lloyd Algorithm 11.

1.13 Quantum Mean Algorithm 11.1.14 Quantum Weighted Sum Algorithm 11.2 Physics of Quantum Algorithms 11.2.1 Implementation of Deutsch''s Algorithm 11.2.

2 Implementation of Deutsch and Jozsa''s Algorithm 11.2.3 Ethan Bernstein and Umesh Vazirani Implementation 11.2.4 Implementation of Quantum Fourier Transform 11.2.5 Estimating Arbitrary Phases 11.2.

6 Improving success probability when estimating phases 11.2.7 The OrderFinding Problem 11.2.8 Concatenated Interference 11.2.9 DESIGN EXAMPLE2): Grover''s algorithm 11.2.

10 DESIGN EXAMPLE3) :Simon''s algorithm 11.2.11 DESIGN EXAMPLE4) : Shor''s Algorithm 11 REFERENCES Ch12. QUANTUM MACHINE LEARNING 12.1 Quantum machine learning algorithms 12.2 Quantum Neural Network Preliminaries 12.3 Quantum Classifiers with ML: Near Term Solutions 12.3.

1 The CircuitCentric Quantum Classifier 12.3.2 Training 12.4 Gradients of Parameterized Quantum Gates 12.5 Classification with Quantum Neural Networks 12.5.1 Representation 12.5.

2 Learning 12.6 Quantum Decision Tree Classifier 12.6.1 Model of the Classifier APPENDIX: Matrix Exponential. 12 REFERENCES Ch 13 QC OPTIMIZATION 13.1 Optimization for hybrid quantum classical algorithms 13.1.1 Quantum Approximate Optimization Algorithm (QAOA) 13.

2. Convex Optimization in Quantum Information Theory 13.2.1 Relative Entropy of Entanglement 13.3 Quantum Algorithms for Combinatorial Optimization Problems 13.4. QC for Linear Systems of Equations 13.4.

1 Algorithm in Brief 13.4.2 Detailed Description of the Algorithm 13.4.3 Error Analysis 13.5 DESIGN EXAMPLE: QC for Multiple Regression 13.5.1 Quantum Circuit 13.

6 Quantum Algorithm for Systems of Nonlinear Differential Equations 13 REFERENCES Ch 14 QUANTUM DECISION THEORY 14.1 Potential Enablers for Qc 14.2 Quantum Game Theory 14.2.1 Definitions 14.2.2 Quantum Games 14.2.

3. DESIGN EXAMPLE: Quantum routing games 14.2.4 Quantum Game for Spectrum Sharing 14.3 Quantum Decision Theory (QDT) 14.3.1 Model: quantum decision theory 14.4 Pre.