Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic
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Название: Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

Автор: Savo G. Glisic

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119790310

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СКАЧАТЬ 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

      8  End User License Agreement

      List of Tables

      1 Chapter 3Table 3.1 Multi‐layer network notation.Table 3.2 Finite impulse response (FIR) multi‐layer network notation.Table 3.3 Variables, for the derivation of gradient withϕ ↔ φ...

      2 Chapter 4Table 4.1 An example of the (discrete) membership functions for both antece...

      3 Chapter 5Table 5.1 [1] Learning algorithm.Table 5.2 Time complexity of the most expensive instructions of the learnin...

      4 Chapter 7Table 7.1 Neural network architecture parameters.

      5 Chapter 8Table 8.1 Operation of aCU gate.Table 8.2 Operational of a Toffoli gate.

      6 Chapter 9Table 9.1 Quantum depolarizing channels.Table 9.2 Maximum number of computational steps that can be performed witho...

      7 Chapter 10Table 10.1 The syndrome table for the two‐qubit code.Table 10.2 The syndrome table for all bit‐flip errors on the three‐qubit co...Table 10.3 The syndrome table for the [[4, 2, 2]] code for all single‐qubit...Table 10.4 The syndrome table for single‐qubit X‐ and Z‐errors on the nine‐...Table 10.5 Stabilizers for the distance 3 planar qubit of Figures 10.9d and...

      8 Chapter 17Table 17.1 Expected number of entanglement swaps for ring and grid network ...Table 17.2 Comparison of the number of qubits that have to be stored in a d...

      List of Illustrations

      1 Chapter 2Figure 2.1 If X and Y are two jointly normally distributed random variables,...Figure 2.2 The regression line for predicting Y* from X* is not the 45° line...Figure 2.3 Decision tree.Figure 2.4 Tree terminology.Figure 2.5 Data classification.Figure 2.6 Classification with outliers.Figure 2.7 Classifiers with nonlinear transformations.СКАЧАТЬ