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Tabla de Contenidos: “…Front Cover -- Quantum Inspired Computational Intelligence: Research and Applications -- Copyright -- Dedication -- Contents -- List of Contributors -- About the Editors -- Foreword -- Preface -- Acknowledgments -- Part I : Research -- Chapter 1: Quantum neural computation of entanglement is robust to noise and decoherence -- 1 Introduction and Literature Background -- 2 Dynamic Learning of an Entanglement Indicator -- 3 Learning with Noise -- 4 Decoherence -- 5 Noise Plus Decoherence -- 6 Conclusions -- Acknowledgments -- References -- Chapter 2: Quantum computing and supervised machine learning: Training, model selection, and error estimation -- 1 Introduction -- 2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation -- 3 Classical and Quantum Computing -- 3.1 Classical Computing -- 3.2 Quantum Computing -- 4 Quantum Computing for Training -- 4.1 Bounded Loss Functions -- 4.1.1 Example: The problems behind the convex relaxation -- 4.2 Energy-Efficient Models -- 4.3 Sparse Solutions -- 4.4 Gibbs and Bayes Classifiers -- 5 Quantum Computing for Model Selection and Error Estimation -- 5.1 Out-of-Sample Methods: Hold-Out, Cross Validation, and Bootstrap -- 5.2 Vapnik-Chervonenkis Theory -- 5.3 (Local) Rademacher Complexity Theory -- 5.4 PAC-Bayes Theory -- 5.4.1 Algorithmic stability -- 5.4.2 Compression bound -- 6 Conclusions -- References -- Chapter 3: Field computation: A framework for quantum-inspired computing -- 1 Introduction -- 2 Fields -- 3 Field computation -- 4 Derivatives of Field Transformations -- 5 Examples of Field Computation -- 5.1 Neural Network-Style Computation -- 5.2 Discrete Basis Function Networks -- 5.3 Continua of Basis Functions -- 5.4 Approximations of Spatial Integration and Differentiation -- 5.5 Iterative Field Computation -- 5.6 Field Differential Equations…”

Tabla de Contenidos: “…4.4.6 Integral Property of AES Reduced to 2.5 Rounds 135 -- 4.4.7 Balanced Property 136 -- 4.4.8 Integral Property of AES Reduced to Three Rounds and Distinguishing Attack 137 -- 4.4.9 Key Recovery Attack with Integral Cryptanalysis for Five Rounds 139 -- 4.4.10 Higher-Order Integral Property + 141 -- 4.4.11 Key Recovery Attack with Integral Cryptanalysis for Six Rounds + 143 -- Further Reading 147 -- 5 Side-Channel Analysis and Fault Analysis on Block Ciphers 149 -- 5.1 Introduction 149 -- 5.1.1 Intrusion Degree of Physical Attacks 149 -- 5.1.2 Passive and Active Noninvasive Physical Attacks 151 -- 5.1.3 Cryptanalysis Compared to Side-Channel Analysis and Fault Analysis 151 -- 5.2 Basics of Side-Channel Analysis 152 -- 5.2.1 Side Channels of Digital Circuits 152 -- 5.2.2 Goal of Side-Channel Analysis 154 -- 5.2.3 General Procedures of Side-Channel Analysis 155 -- 5.2.4 Profiling versus Non-profiling Side-Channel Analysis 156 -- 5.2.5 Divide-and-Conquer Algorithm 157 -- 5.3 Side-Channel Analysis on Block Ciphers 159 -- 5.3.1 Power Consumption Measurement in Power Analysis 160 -- 5.3.2 Simple Power Analysis and Differential Power Analysis 163 -- 5.3.3 General Key Recovery Algorithm for DPA 164 -- 5.3.4 Overview of Attack Targets 169 -- 5.3.5 Single-Bit DPA Attack on AES-128 Hardware Implementations 181 -- 5.3.6 Attacks Using HW Model on AES-128 Hardware Implementations 186 -- 5.3.7 Attacks Using HD Model on AES-128 Hardware Implementations 192 -- 5.3.8 Attacks with Collision Model + 199 -- 5.4 Basics of Fault Analysis 203 -- 5.4.1 Faults Caused by Setup-Time Violations 205 -- 5.4.2 Faults Caused by Data Alternation 208 -- 5.5 Fault Analysis on Block Ciphers 208 -- 5.5.1 Differential Fault Analysis 208 -- 5.5.2 Fault Sensitivity Analysis + 215 -- Acknowledgment 223 -- Bibliography 223 -- 6 Advanced Fault Analysis with Techniques from Cryptanalysis 225 -- 6.1 Optimized Differential Fault Analysis 226 -- 6.1.1 Relaxing Fault Model 226 -- 6.1.2 Four Classes of Faulty Byte Positions 227.…”

Tabla de Contenidos: “…Synchronization-based objective function -- 6.3.2 Optimization of Topology -- Greedy algorithm -- Relaxed optimization -- Decomposition-based optimization -- Structure-based optimization -- 6.4 Team decision theory -- 6.4.1 Team Decision Theory -- System model -- Values of multiple information structures -- Information structures in networks -- 6.4.2 Team Decision Theory in Optimal Control -- System model -- State teams -- Dynamic teams -- 6.5 Conclusions -- Chapter 7: Communication network operation for CPSs -- 7.1 Introduction -- 7.1.1 Main Challenges -- 7.1.2 Main Approaches -- 7.2 Hybrid system modeling for CPSs -- 7.2.1 Hybrid Systems -- Linear switching system -- Control of linear switching systems -- 7.2.2 Hybrid System Model of a CPS -- Communication mode -- Relationship between communication and dynamics modes -- 7.3 Optimization of scheduling policy -- 7.3.1 Fundamental Challenges -- 7.3.2 Mode Provisioning -- Generic procedure -- Illustration by examples -- 7.3.3 Mode Scheduling -- Centralized mode scheduling -- Distributed scheduling -- 7.4 Scheduling: other approaches -- 7.4.1 Optimization-Based Scheduling -- System model -- Communication constraints -- 7.4.2 Effective Information-Based Scheduling -- Delay-tolerant Kalman filtering -- Definition of virtual queues -- Information bits -- Distributed scheduling -- 7.5 Routing -- 7.5.1 Estimation Oriented Routing -- System model -- Encoder and decoder -- Evolution of covariance -- 7.5.2 System Dynamics-Aware Multicast Routing -- System model -- Single mode routing -- Multiple routing modes -- 7.6 Conclusions -- Chapter 8: Physical layer design -- 8.1 Introduction -- 8.1.1 Modulation -- 8.1.2 Coding -- Source coding -- Channel coding -- 8.2 Adaptive modulation -- 8.2.1 System Model -- Physical dynamics -- Communication channel…”

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