000			05414cam a2200589Ia 4500
001			ocn890854311
003			OCoLC
005			20190328114808.0
006			m o d
007			cr cnu---unuuu
008			140303s2014 cau o 000 0 eng d
040			_aIDEBK _beng _epn _cIDEBK _dOCLCO _dN$T _dE7B _dUIU _dCHVBK _dOCLCF _dOCLCQ _dNRC _dOCLCQ _dUAB _dK6U _dCOO _dLIV _dD6H _dVVB _dU3W _dWYU
020			_a9780128010990 _q(electronic bk.)
020			_a0128010991 _q(electronic bk.)
020			_a132211434X _q(electronic bk.)
020			_a9781322114347 _q(electronic bk.)
020			_z9780128009536
035			_a(OCoLC)890854311
050		4	_aQC174.12
072		7	_aSCI _x024000 _2bisacsh
072		7	_aSCI _x041000 _2bisacsh
072		7	_aSCI _x055000 _2bisacsh
082	0	4	_a530.12 _223
100	1		_aWittek, Peter, _eauthor.
245	1	0	_aQuantum machine learning : what quantum computing means to data mining / _h[electronic resource] _cPeter Wittek.
250			_a1st ed.
264		1	_aSan Diego, CA : _bAcademic Press, an imprint of Elsevier, _c2014.
300			_a1 online resource (176 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aPrint version record.
505	0		_aFront Cover; Quantum Machine Learning: What Quantum Computing Meansto Data Mining; Copyright; Contents; Preface; Notations; Part One Fundamental Concepts; Chapter 1: Introduction; 1.1Learning 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; Chapter 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.
505	8		_a2.7. Data Dependencies and Computational ComplexityChapter 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; Chapter 4:Quantum Computing; 4.1. Qubits and the Bloch Sphere; 4.2. Quantum Circuits; 4.3. Adiabatic Quantum Computing; 4.4. Quantum Parallelism; 4.5. Grover''s Algorithm; 4.6.Complexity Classes; 4.7. Quantum Information Theory; Part Two Classical Learning Algorithms.
505	8		_aChapter 5:Unsupervised Learning5.1. Principal Component Analysis; 5.2. Manifold Embedding; 5.3.K-Means and K-Medians Clustering; 5.4. Hierarchical Clustering; 5.5. Density-Based Clustering; Chapter 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; Chapter 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 Kernel Functions; 7.5. Least-Squares Formulation; 7.6. Generalization Performance.
505	8		_a7.7. Multiclass Problems7.8. Loss Functions; 7.9.Computational Complexity; Chapter 8:Regression Analysis; 8.1. Linear Least Squares; 8.2. Nonlinear Regression; 8.3. Nonparametric Regression; 8.4.Computational Complexity; Chapter 9:Boosting; 9.1. Weak Classifiers; 9.2. AdaBoost; 9.3.A Family of Convex Boosters; 9.4. Nonconvex Loss Functions; Part Three Quantum Computing and Machine Learning; Chapter 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.
505	8		_a10.5. Quantum K-Means10.6. Quantum K-Medians; 10.7. Quantum Hierarchical Clustering; 10.8.Computational Complexity; Chapter 11:Quantum Pattern Recognition; 11.1. Quantum Associative Memory; 11.2. The Quantum Perceptron; 11.3. Quantum Neural Networks; 11.4. Physical Realizations; 11.4.Computational Complexity; Chapter 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; Chapter 13:Quantum Process Tomography and Regression; 13.1. Channel-State Duality.
520			_aBridging the gap between abstract developments in quantum computing and the applied research on machine learning, this book pares down the complexity of the disciplines involved, and focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. -- _cEdited summary from book.
650		0	_aQuantum theory.
650		0	_aData mining.
650		7	_aSCIENCE _xEnergy. _2bisacsh
650		7	_aSCIENCE _xMechanics _xGeneral. _2bisacsh
650		7	_aSCIENCE _xPhysics _xGeneral. _2bisacsh
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aQuantum theory. _2fast _0(OCoLC)fst01085128
650		7	_aMaschinelles Lernen. _0(DE-588)4193754-5 _2gnd
650		7	_aQuanteninformatik. _0(DE-588)4705961-8 _2gnd
650		7	_aData Mining. _0(DE-588)4428654-5 _2gnd
655		4	_aElectronic books.
776	0	8	_iPrint version: _aWittek, Peter author. _tQuantum Machine Learning. _d[San Diego, CA] : Academic Press, 2014 _z9780128009536
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128009536
999			_c246957 _d246957