量子机器学习中数据挖掘的量子计算方法(英文版)[匈]维特克（Wittek P.）哈尔滨工业大学出版社【现货实拍 可 2025 chm pdf kindle rb azw3 下载 115盘

　　Machine learning is a fascinating area to work in： from detecting anomalous events in live streams of sensor data to identifying emergent topics involving text collection，exciting problems are never too far away.

Quantuminformation theory also teems with excitement. By manipulating particles at a subatomic level， we are able to perform Fourier transformation exponentially faster， or search in a database quadratically faster than the classical limit. Superdense coding transmits two classical bits using just one qubit. Quantum encryption is unbreakable-at least in theory.

Preface

Notations

Part One 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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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。