量子机器学习中数据挖掘的量子计算方法（英文版） / 国外优秀物理著作原版系列
作者： (匈)维特克
出版时间：2016年1月
出版社：哈尔滨工业大学出版社
- 哈尔滨工业大学出版社
- 9787560357591
- 81868
- 2016年1月
- 未分类
- 未分类
- O413.1-39
维特克著的《量子机器学习中数据挖掘的量子计算方法(英文版)/国外优秀物理著作原版系列》分三个部分对量子机器学习中数据挖掘的量子计算方法进行了介绍，第一部分对基础概念进行了整体概述，例如，机器学习、量子力学、量子计算等，第二部分介绍了经典的学习算法，第三部分介绍了量子计算与机器学习。这本书综合了广泛的调查研究形成，采用简洁的表达形式，并配以应用、实践的例子。
Preface
Notations
Part One Fundamental Concepts
1 Introduction
1.1 Learning Theory and Data Mining
1.2 Why Quantum Computers？
1.3 A Heterogeneous Model
1.4 An Overview of Quantum Machine Learning Algorithms
1.5 Quantum—Like Learning on Classical Computers
2 Machine Learning
2.1 Data—DrivenModels
2.2 FeatureSpace
2.3 Supervised and Unsupervised Learning
2.4 Generalization Performance
2.5 Model Complexity
2.6 Ensembles
2.7 Data Dependencies and Computational Complexity
3 Quantum Mechanics
3.1 States and Superposition
3.2 Density Matrix Representation and Mixed States
3.3 Composite Systems and Entanglement
3.4 Evolution
3.5 Measurement
3.6 Uncertainty Relations
3.7 Tunneling
3.8 Adiabatic Theorem
3.9 No—Cloning Theorem
4 Quantum Computing
4.1 Qubits and the Bloch Sphere
4.2 QuantumCircuits
4.3 Adiabatic Quantum Computing
4.4 QuantumParallelism
4.5 Grover's Algorithm
4.6 Complexity Classes
4.7 Quantum Information Theory
Part Two Classical Learning Algorithms
5 Unsupervised Learning
5.1 Principal Component Analysis
5.2 ManifoldEmbedding
5.3 K—Means and K—Medians Clustering
5.4 Hierarchical Clustering
5.5 Density—BasedClustering
6 Pattern Recogrution and Neural Networks
6.1 The Perceptron
6.2 Hopfield Networks
6.3 Feedforward Networks
6.4 Deep Learning
6.5 Computational Complexity
7 Supervised Learning and Support Vector Machines
7.1 K—Nearest Neighbors
7.2 Optimal Margin Classifiers
7.3 Soft Margins
7.4 Nonlinearity and KemelFunctions
7.5 Least—Squares Formulation
7.6 Generalization Performance
7.7 Multiclass Problems
7.8 Loss Functions
7.9 Computational Complexity
8 Regression Analysis
8.1 Linear Least Squares
8.2 Nonlinear Regression
8.3 Nonparametric Regression
8.4 Computational Complexity
9 Boosting
9.1 Weak Classifiers
9.2 Ada Boost
9.3 A Family of Convex Boosters
9.4 Nonconvex Loss Functions
Part Three Quantum Computing and Machine Learning
10 Clustering Structure and Quantum Computing
10.1 Quantum Random Access Memory
10.2 Calculating Dot Products
10.3 Quantum Principal Component Analysis
10.4 Toward Quantum Manifold Embedding
10.5 QuantumK—Means
10.6 QuantumK—Medians
10.7 Quantum Hierarchical Clustering
10.8 Computational Complexity
11 Quantum Pattern Recognition
11.1 Quantum Associative Memory
11.2 The Quantum Perceptron
11.3 Quantum Neural Networks
11.4 Physical Realizations
11.5 Computational Complexity
12 Quantum Classification
12.1 Nearest Neighbors
12.2 Support Vector Machines with Grover's Search
12.3 Support Vector Machines with Exponential Speedup
12.4 Computational Complexity
13 Quantum Process Tomography and Regression
13.1 Channel—State Duality
13.2 Quantum Process Tomography
13.3 Groups， Compact Lie Groups， and the Unitary Group
13.4 Representation Theory
13.5 Parallel Application and Storage of the Unitary
13.6 Optimal State for Learning
13.7 Applying the Unitary and Finding the Parameter for the Input State
14 Boosting and Adiabatic Quantum Computing
14.1 Quantum Annealing
14.2 Quadratic Unconstrained Binary Optimization
14.3 Ising Model
14.4 QBoost
14.5 Nonconvexity
14.6 Sparsity， Bit Depth， and Generalization Performance
14.7 Mapping to Hardware
14.8 Computational Complexity
Bibliography
