Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 2
Table of Contents
Оглавление1 Cover
4 Preface
5 Part I: Artificial Intelligence 1 Introduction 1.1 Motivation 1.2 Book Structure References 2 Machine Learning Algorithms 2.1 Fundamentals 2.2 ML Algorithm Analysis References 3 Artificial Neural Networks 3.1 Multi‐layer Feedforward Neural Networks 3.2 FIR Architecture 3.3 Time Series Prediction 3.4 Recurrent Neural Networks 3.5 Cellular Neural Networks (CeNN) 3.6 Convolutional Neural Network (CoNN) References 4 Explainable Neural Networks 4.1 Explainability Methods 4.2 Relevance Propagation in ANN 4.3 Rule Extraction from LSTM Networks 4.4 Accuracy and Interpretability References 5 Graph Neural Networks 5.1 Concept of Graph Neural Network (GNN) 5.2 Categorization and Modeling of GNN 5.3 Complexity of NN Appendix 5.A Notes on Graph Laplacian Appendix 5.B Graph Fourier Transform References 6 Learning Equilibria and Games 6.1 Learning in Games 6.2 Online Learning of Nash Equilibria in Congestion Games 6.3 Minority Games 6.4 Nash Q‐Learning 6.5 Routing Games 6.6 Routing with Edge Priorities References 7 AI Algorithms in Networks 7.1 Review of AI‐Based Algorithms in Networks 7.2 ML for Caching in Small Cell Networks 7.3 Q‐Learning‐Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks 7.4 ML for Self‐Organizing Cellular Networks 7.5 RL‐Based Caching 7.6 Big Data Analytics in Wireless Networks 7.7 Graph Neural Networks 7.8 DRL for Multioperator Network Slicing 7.9 Deep Q‐Learning for Latency‐Limited Network Virtualization 7.10 Multi‐Armed Bandit Estimator (MBE) 7.11 Network Representation Learning References
6 Part II: Quantum Computing 8 Fundamentals of Quantum Communications 8.1 Introduction 8.2 Quantum Gates and Quantum Computing 8.3 Quantum Fourier Transform (QFT) References 9 Quantum Channel Information Theory 9.1 Communication Over a Channel 9.2 Quantum Information Theory 9.3 Channel Description 9.4 Channel Classical Capacities 9.5 Channel Quantum Capacity 9.6 Quantum Channel Examples References 10 Quantum Error Correction 10.1 Stabilizer Codes 10.2 Surface Code 10.3 Fault‐Tolerant Gates 10.4 Theoretical Framework 10.A Binary Fields and Discrete Vector Spaces 10.B Some Noise Physics References 11 Quantum Search Algorithms 11.1 Quantum Search Algorithms 11.2 Physics of Quantum Algorithms References 12 Quantum Machine Learning 12.1 QML Algorithms 12.2 QNN Preliminaries 12.3 Quantum Classifiers with ML: Near‐Term Solutions 12.4 Gradients of Parameterized Quantum Gates 12.5 Classification with QNNs 12.6 Quantum Decision Tree Classifier Appendix 12.7 Matrix Exponential References 13 QC Optimization 13.1 Hybrid Quantum‐Classical Optimization Algorithms 13.2 Convex Optimization in Quantum Information Theory 13.3 Quantum Algorithms for Combinatorial Optimization Problems 13.4 QC for Linear Systems of Equations 13.5 Quantum Circuit 13.6 Quantum Algorithm for Systems of Nonlinear Differential Equations References 14 Quantum Decision Theory 14.1 Potential Enablers for Qc 14.2 Quantum Game Theory (QGT) 14.3 Quantum Decision Theory (QDT) 14.4 Predictions in QDT References 15 Quantum Computing in Wireless Networks 15.1 Quantum Satellite Networks 15.2 QC Routing for Social Overlay Networks 15.3 QKD Networks References 16 Quantum Network on Graph 16.1 Optimal Routing in Quantum Networks 16.2 Quantum Network on Symmetric Graph 16.3 QWs 16.4 Multidimensional QWs References 17 Quantum Internet 17.1 System Model 17.2 Quantum Network Protocol Stack References
7 Index
