Quantum machine learning: what quantum computing means to data mining
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston ; Heidelberg
Academic Press
[2014]
|
| Ausgabe: | First edition |
| Schriftenreihe: | Elsevier insights
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | x, 163 Seiten |
| ISBN: | 9780128100400 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804178417659150336 |
|---|---|
| adam_text | Contents Preface Notations ix xi Part One Fundamental Concepts 1 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 3 5 6 7 7 9 2 Machine Learning 2.1 Data-Driven Models 2.2 Feature Space 2.3 Supervised and Unsupervised Learning 2.4 Generalization Performance 2.5 Model Complexity 2.6 Ensembles 2.7 Data Dependencies and Computational Complexity 11 12 12 15 18 20 22 23 3 Quantum Mechanics 3.1 States and Superposition 3.2 Density Matrix Representation andMixed 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 25 26 27 29 32 34 36 37 37 38 4 Quantum Computing 4.1 Qubits and the Bloch Sphere 4.2 Quantum Circuits 4.3 Adiabatic Quantum Computing 4.4 Quantum Parallelism 41 41 44 48 49
vi Contents 4.5 4.6 4.7 Grover’s Algorithm Complexity Classes Quantum Information Theory Part Two Classical Learning Algorithms 49 51 52 55 5 Unsupervised Learning 5.1 Principal Component Analysis 5.2 Manifold Embedding 5.3 K-Means and Äľ-Medians Clustering 5.4 Hierarchical Clustering 5.5 Density-Based Clustering 57 57 58 59 60 61 6 Pattern Recognition and Neural Networks 6.1 The Perceptron 6.2 Hopfield Networks 6.3 Feedforward Networks 6.4 Deep Learning 6.5 Computational Complexity 63 63 65 67 69 70 7 Supervised Learning and Support Vector Machines 7.1 К-Nearest Neighbors 7.2 Optimal Margin Classifiers 7.3 Soft Margins 7.4 Nonlinearity and Kernel Functions 7.5 Least-Squares Formulation 7.6 Generalization Performance Ί.Ί Multiclass Problems 7.8 Loss Functions 7.9 Computational Complexity 73 74 74 76 77 80 81 81 83 83 8 Regression Analysis 8.1 Linear Least Squares 8.2 Nonlinear Regression 8.3 Nonparametric Regression 8.4 Computational Complexity 85 85 86 87 87 9 Boosting 9.1 Weak Classifiers 9.2 AdaBoost 9.3 A Family of Convex Boosters 9.4 Nonconvex Loss Functions 89 89 90 92 94
vii Contents Part Three Quantum Computing andMachine 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 10.5 10.6 10.7 10.8 11 Quantum Pattern Recognition 11.1 11.2 11.3 11,4 11.5 12 Nearest Neighbors Support Vector Machines with Grover’s Search Support Vector Machines with Exponential Speedup Computational Complexity Quantum Process Tomography and Regression 13.1 Channel-State Duality 13.2 13.3 13.4 13.5 13.6 13.7 14 Quantum Associative Memory The Quantum Perceptron Quantum Neural Networks Physical Realizations Computational Complexity Quantum Classification 12.1 12.2 12.3 12.4 13 Toward Quantum Manifold Embedding Quantums-Means Quantum S-Medians Quantum Hierarchical Clustering Computational Complexity Quantum Process Tomography Groups, Compact Lie Groups, and the Unitary Group Representation Theory Parallel Application and Storage of the Unitary Optimal State for Learning Applying the Unitary and Finding the Parameter for the Input State Boosting and Adiabatic Quantum Computing 14. ! 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 97 99 99 100 102 104 104 105 106 107 109 109 114 115 116 118 119 119 121 122 123 125 126 127 128 130 133 134 136 139 140 141 142 143 143 145 147 151 153
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| any_adam_object | 1 |
| author | Wittek, Peter |
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| author_facet | Wittek, Peter |
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| author_sort | Wittek, Peter |
| author_variant | p w pw |
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| dewey-full | 530.12 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 530 - Physics |
| dewey-raw | 530.12 |
| dewey-search | 530.12 |
| dewey-sort | 3530.12 |
| dewey-tens | 530 - Physics |
| discipline | Physik Informatik |
| edition | First edition |
| format | Book |
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| isbn | 9780128100400 |
| language | English |
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| spelling | Wittek, Peter Verfasser (DE-588)1122570023 aut Quantum machine learning what quantum computing means to data mining Peter Wittek, University of Borås, Schweden First edition Amsterdam ; Boston ; Heidelberg Academic Press [2014] © 2014 x, 163 Seiten txt rdacontent n rdamedia nc rdacarrier Elsevier insights Quantentheorie Quantum theory Data mining Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Quantencomputer (DE-588)4533372-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Quantencomputer (DE-588)4533372-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Data Mining (DE-588)4428654-5 s 1\p DE-604 Maschinelles Lernen (DE-588)4193754-5 s 2\p DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030274342&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Wittek, Peter Quantum machine learning what quantum computing means to data mining Quantentheorie Quantum theory Data mining Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd Quantencomputer (DE-588)4533372-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4428654-5 (DE-588)4533372-5 (DE-588)4033447-8 |
| title | Quantum machine learning what quantum computing means to data mining |
| title_auth | Quantum machine learning what quantum computing means to data mining |
| title_exact_search | Quantum machine learning what quantum computing means to data mining |
| title_full | Quantum machine learning what quantum computing means to data mining Peter Wittek, University of Borås, Schweden |
| title_fullStr | Quantum machine learning what quantum computing means to data mining Peter Wittek, University of Borås, Schweden |
| title_full_unstemmed | Quantum machine learning what quantum computing means to data mining Peter Wittek, University of Borås, Schweden |
| title_short | Quantum machine learning |
| title_sort | quantum machine learning what quantum computing means to data mining |
| title_sub | what quantum computing means to data mining |
| topic | Quantentheorie Quantum theory Data mining Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd Quantencomputer (DE-588)4533372-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Quantentheorie Quantum theory Data mining Maschinelles Lernen Data Mining Quantencomputer Künstliche Intelligenz |
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