《经典与量子信息论(电子学与通信技术英文版)/国外信息科学与技术优秀图书系列》编著者Emmanuel Desurvire。

信息理论处于现代技术的核心位置，是所有通信系统、网络系统和数据存储系统的基础。本书首次完整叙述了经典信息论和量子信息论。自始至终，本书都在向读者介绍重要的结论，而不是让读者迷失在数学推导的细节中。刚开始的几章介绍了香农熵的基本概念和各种应用，然后介绍量子信息和量子计算的核心特点。从经典信息论和量子信息论的角度，介绍了编码、压缩、纠错、加密和信道容量等论题。采用非正式但科学的精确方法，作者为读者提供了理解量子门和电路的知识。本书配有大量的实践案例和章后习题，适合电子工程和计算机科学专业的研究生和研究人员学习，也可供通信行业的从业者参考。

《经典与量子信息论(电子学与通信技术英文版)/国外信息科学与技术优秀图书系列》编著者Emmanuel Desurvire。

《经典与量子信息论(电子学与通信技术英文版)/国外信息科学与技术优秀图书系列》内容提要：完整地叙述了经典信息论和量子信息论，首先介绍了香农熵的基本概念和各种应用，然后介绍了量子信息和量子计算的核心特点。本书从经典信息论和量子信息论的角度，介绍了编码、压缩、纠错、加密和信道容量等内容，采用非正式但科学的精确方法，为读者提供了理解量子门和电路的知识。

本书自始至终都在向读者介绍重要的结论，而不是让读者迷失在数学推导的细节中，并且配有大量的实践案例和章后习题，适合电子、通信、计算机等专业的研究生和科研人员学习参考。

Foreword

Introduction

1 Probability basics

 1.1 Events, event space, and probabilities

 1.2 Combinatorics

 1.3 Combined, joint, and conditional probabilities

 1.4 Exercises

2 Probability distributions

 2.1 Mean and variance

 2.2 Exponential, Poisson, and binomial distributions

 2.3 Continuous distributions

 2.4 Uniform, exponential, and Gaussian (normal) distributions

 2.5 Central-limit theorem

 2.6 Exercises

3 Measuring information

 3.1 Making sense of information

 3.2 Measuring information

 3.3 Information bits

 3.4 Renyi's fake coin

 3.5 Exercises

4 Entropy

 4.1 From Boltzmann to Shannon

 4.2 Entropy in dice

 4.3 Language entropy

 4.4 Maximum entropy (discrete source)

 4.5 Exercises

7 Mutual information and more entropies

 5.1 Joint and conditional entropies

 5.2 Mutual information

 5.3 Relative entropy

 5.4 Exercises

6 Differential entropy

 6.1 Entropy of continuous sources

 6.2 Maximum entropy (continuous source)

 6.3 Exercises

7 Algorithmic entropy and Kolmogorov complexity

 7.1 Defining algorithmic entropy

 7.2 The Turing machine

 7.3 Universal Turing machine

 7.4 Kolmogorov complexity

 7.5 Kolmogorov complexity vs. Shannon's entropy

 7.6 Exercises

8 Information coding

 8.1 Coding numbers

 8.2 Coding language

 8.3 The Morse code

 8.4 Mean code length and coding efficiency

 8.5 Optimizing coding efficiency

 8.6 Shannon's source-coding theorem

 8.7 Exercises

9 Optimal coding and compression

 9.1 Huffman codes

 9.2 Data compression

 9.3 Block codes

 9.4 Exercises

10 Integer, arithmetic, and adaptive coding

 10.1 Integer coding

 10.2 Arithmetic coding

 10.3 Adaptive Huffman coding

 10.4 Lempel-Ziv coding

 10.5 Exercises

11 Error correction

 l 1.1 Communication channel

 11.2 Linear block codes

 11.3 Cyclic codes

 11.4 Error-correction code types

 11.5 Corrected bit-error-rate

 11.6 Exercises

12 Channel entropy

 12.1 Binary symmetric channel

 12.2 Nonbinary and asymmetric discrete channels

 12.3 Channel entropy and mutual information

 12.4 Symbol error rate

 12.5 Exercises

13 Channel capacity and coding theorem

 13.1 Channel capacity

 13.2 Typical sequences and the typical set

 13.3 Shannon's channel coding theorem

 13.4 Exercises

14 Gaussian channel and Shannon-Hartley theorem

 14.1 Gaussian channel

 14.2 Nonlinear channel

 14.3 Exercises

15 Reversible computation

 15.1 Maxwell's demon and Landauer's principle

 15.2 From computer architecture to logic gates

 15.3 Reversible logic gates and computation

 15.4 Exercises

16 Quantum bits and quantum gates

 16.1 Quantum bits

 16.2 Basic computations with 1-qubit quantum gates

 16.3 Quantum gates with multiple qubit inputs and outputs

 16.4 Quantum circuits

 16.5 Tensor products

 16.6 Noncloning theorem

 16.7 Exercises

17 Quantum measurements

 17.1 Dirac notation

 17.2 Quantum measurements and types

 17.3 Quantum measurements on joint states

 17.4 Exercises

18 Qubit measurements, superdense coding, and quantum teleportaUon

 18.1 Measuring single qubits

 18.2 Measuring n-qubits

 18.3 Bell state measurement

 18.4 Superdense coding

 18.5 Quantum teleportation

 18.6 Distributed quantum computing

 18.7 Exercises

19 Deutsch-Jozsa, quantum Fourier transform, and Grover quantum database

 search algorithms

 19. l Deutsch algorithm

 19.2 Deutsch-Jozsa algorithm

 19.3 Quantum Fourier transform algorithm

 19.4 Grover quantum database search algorithm

 19.5 Exercises

20 Shor's factorization algorithm

 20.1 Phase estimation

 20.2 Order finding

 20.3 Continued fraction expansion

 20.4 From order finding to factorization

 20.5 Shor's factorization algorithm

 20.6 Factorizing N = 15 and other nontrivial composites

 20.7 Public-key cryptography

 20.8 Exercises

21 Quantum information theory

 21.1 Von Neumann entropy

 21.2 Relative, joint, and conditional entropy, and mutual information

 21.3 Quantum communication channel and Holevo bound

 21.4 Exercises

 ……