¥ 85 5.4折 ¥ 158 九品
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作者[法]Emmanuel Desurvire 著
出版社科学出版社
出版时间2013-01
版次1
装帧平装
货号E2-207
上书时间2023-07-14
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
5 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
11.1 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
19.1 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
……
25 Classical and quantum cryptography
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