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《量子机器学习中数据挖掘的量子计算方法(英文版)》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，第壹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。

目录

Preface 

Notations 

PartOne FundamentaIConcepts 

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 GeneralizationPerformance 

2.5 ModeIComplexity 

2.6 Ensembles 

2.7 Data Dependencies and ComputationalComplexity 

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 UncertaintyRelations 

3.7 Tunneling 

3.8 Adiabatic Theorem 

3.9 No—CloningTheorem 

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 ComplexityClasses 

4.7 QuantumInformationTheory 

Part Two ClassicalLearning Algorithms 

5 Unsupervised Learning 

5.1 Principal Component Analysis 

5.2 ManifoldEmbedding 

5.3 K—Means and K—Medians Clustering 

5.4 HierarchicalClustering 

5.5 Density—BasedClustering 

6 Pattern Recogrution and Neural Networks 

6.1 ThePerceptron 

6.2 HopfieldNetworks 

6.3 FeedforwardNetworks 

6.4 DeepLearning 

6.5 ComputationalComplexity 

7 Supervised Learning and Support Vector Machines 

7.1 K—NearestNeighbors 

7.20ptimal Margin Classifiers 

7.3 SoftMargins 

7.4 Nonlinearity and KemelFunctions 

7.5 Least—SquaresFormulation 

7.6 Generalization Performance 

7.7 Multiclass Problems 

7.8 Loss Functions 

7.9 ComputationalComplexity 

8 Regression Analysis 

8.1 Linear Least Squares 

8.2 NonlinearRegression 

8.3 NonparametricRegression 

8.4 ComputationalComplexity 

9 Boosting 

9.1 WeakClassifiers 

9.2 AdaBoost 

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 ComputationalComplexity 

11 Quantum Pattern Recognition 

11.1 Quantum Associative Memory 

11.2 The Quantum Perceptron 

11.3 Quantum Neural Networks 

11.4 PhysicaIRealizations 

11.5 ComputationalComplexity 

12 QuantumClassification 

12.1 Nearest Neighbors 

12.2 Support Vector Machines with Grover's Search 

12.3 Support Vector Machines with Exponential Speedup 

12.4 ComputationalComplexity 

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 ComputationalComplexity 

Bibliography

作者：（匈牙利）维特克（Wittek P.）

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Recent advances in quantum information theory indicate that machine leamingmay benefit from various paradigms of the field.For instance， adiabatic quantumcomputing finds the minimum of a multivariate function by a controlled physicalprocess using the adiabatic theorem （Farhi et al.， 2000）.The function is translated toa physical description， the Hamiltonian operator of a quantum system.Then， a systemwith a simple Hamiltonian is prepared and initialized to the ground state， the lowestenergy state a quantum system can occupy.Finally， the simple Hamiltonian is evolvedto the target Hamiltonian， and， by the adiabatic theorem， the system remains in theground state.At the end of the process， the solution is read out from the system， andwe obtain the global optimum for the function in question. 

While more and more articles that explore the intersection of quantum computingand machine learning are being published， the field is fragmented， as was alreadynoted over a decade ago （Bonner and Freivalds， 2002）.This should not come as asurprise： machine learning itself is a diverse and fragmented field of inquiry.Weattempt to identify common algorithms and trends， and observe the subtle interplaybetween faster execution and improved performance in machine learning by quantumcomputing.

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《量子机器学习中数据挖掘的量子计算方法(英文版)》由哈尔滨工业大学出版社出版。

书籍介绍

《量子机器学习中数据挖掘的量子计算方法(英文版)》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，第壹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。