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量子机器学习中数据挖掘的量子计算方法PDF|Epub|txt|kindle电子书版本网盘下载
- （匈）维特克（P·Wittek）著 著
- 出版社： 哈尔滨：哈尔滨工业大学出版社
- ISBN：7560357591
- 出版时间：2016
- 标注页数：164页
- 文件大小：24MB
- 文件页数：187页
- 主题词：
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图书目录
Part One Fundamental Concepts1
1 Introduction3
1.1 Learning Theory and Data Mining5
1.2 Why Quantum Computers?6
1.3 AHeterogeneous Model7
1.4 An Overview of Quantum Machine Learning Algorithms7
1.5 Quantum-Like Learning on Classical Computers9
2 Machine Learning11
2.1 Data-Driven Models12
2.2 Feature Space12
2.3 Supervised and Unsupervised Learning15
2.4 Generalization Performance18
2.5 Model Complexity20
2.6 Ensembles22
2.7 Data Dependencies and Computational Complexity23
3 Quantum Mechanics25
3.1 States and Superposition26
3.2 Density Matrix Representation and Mixed States27
3.3 Composite Systems and Entanglement29
3.4 Evolution32
3.5 Measurement34
3.6 Uncertainty Relations36
3.7 Tunneling37
3.8 Adiabatic Theorem37
3.9 No-Cloning Theorem38
4 Quantum Computing41
4.1 Qubits and the Bloch Sphere41
4.2 Quantum Circuits44
4.3 Adiabatic Quantum Computing48
4.4 Quantum Parallelism49
4.5 Grover's Algorithm49
4.6 Complexity Classes51
4.7 Quantum Information Theory52
Part Two Classical Learning Algorithms55
5 Unsupervised Learning57
5.1 Principal Component Analysis57
5.2 Manifold Embedding58
5.3 K-Means and K-Medians Clustering59
5.4 Hierarchical Clustering60
5.5 Density-Based Clustering61
6 Pattern Recognition and Neural Networks63
6.1 The Perceptron63
6.2 Hopfield Networks65
6.3 Feedforward Networks67
6.4 DeepLearning69
6.5 Computational Complexity70
7 Supervised Learning and Support Vector Machines73
7.1 K-Nearest Neighbors74
7.2 Optimal Margin Classifiers74
7.3 Soft Margins76
7.4 Nonlinearity and Kernel Functions77
7.5 Least-Squares Formulation80
7.6 Generalization Performance81
7.7 Multiclass Problems81
7.8 Loss Functions83
7.9 Computational Complexity83
8 Regression Analysis85
8.1 LinearLeast Squares85
8.2 Nonlinear Regression86
8.3 Nonparametric Regression87
8.4 Computational Complexity87
9 Boosting89
9.1 Weak Classifers89
9.2 AdaBoost90
9.3 A Family of Convex Boosters92
9.4 Nonconvex Loss Functions94
Part Three Quantum Computing and Machine Learning97
10 Clustering Structure and Quantum Computing99
10.1 Quantum Random Access Memory99
10.2 Calculating Dot Products100
10.3 Quantum Principal Component Analysis102
10.4 Toward Quantum Manifold Embedding104
10.5 QuantumK-Means104
10.6 Quantum K-Medians105
10.7 Quantum Hierarchical Clustering106
10.8 Computational Complexity107
11 Quantum Pattern Recognition109
11.1 Quantum Associative Memory109
11.2 The Quantum Perceptron114
11.3 QuantumNeural Networks115
11.4 Physical Realizations116
11.5 Computational Complexity118
12 Quantum Classification119
12.1 Nearest Neighbors119
12.2 Support Vector Machines with Grover's Search121
12.3 Support Vector Machines with Exponential Speedup122
12.4 Computational Complexity123
13 Quantum Process Tomography and Regression125
13.1 Channel-State Duality126
13.2 Quantum Process Tomography127
13.3 Groups,Compact Lie Groups,and the Unitary Group128
13.4 Representation Theory130
13.5 Parallel Application and Storage of the Unitary133
13.6 Optimal State for Learning134
13.7 Applying the Unitary and Finding the Parameter for the Input State136
14 Boosting and Adiabatic Quantum Computing139
14.1 Quantum Annealing140
14.2 Quadratic Unconstrained Binary Optimization141
14.3 Ising Model142
14.4 QBoost143
14.5 Nonconvexity143
14.6 Sparsity,Bit Depth,and Generalization Performance145
14.7 Mapping to Hardware147
14.8 Computational Complexity151
Bibliography153