LEADER 05390nam 2200649 450 001 996426328203316 005 20190721112312.0 010 $a0-12-801099-1 035 $a(CKB)2550000001352487 035 $a(EBL)1790890 035 $a(SSID)ssj0001377357 035 $a(PQKBManifestationID)12603891 035 $a(PQKBTitleCode)TC0001377357 035 $a(PQKBWorkID)11318734 035 $a(PQKB)11007458 035 $a(MiAaPQ)EBC1790890 035 $a(EXLCZ)992550000001352487 100 $a20140920h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuantum machine learning $ewhat quantum computing means to data mining /$fPeter Wittek 205 $aFirst edition 210 1$aSan Diego, California :$cAcademic Press,$d2014 210 4$dİ2014 215 $a1 online resource (176 p.) 225 1 $aElsevier Insights 300 $aDescription based upon print version of record 311 $a0-12-800953-5 311 $a1-322-11434-X 320 $aIncludes bibliographical references 327 $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 327 $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 327 $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 327 $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 327 $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 327 $a13.2.Quantum Process Tomography 330 $aQuantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine L 410 0$aElsevier insights. 606 $aMachine learning$xMathematical models 606 $aData mining$xData processing 606 $aQuantum theory$xData processing 608 $aElectronic books 615 0$aMachine learning$xMathematical models. 615 0$aData mining$xData processing. 615 0$aQuantum theory$xData processing. 676 $a621.3822 700 $aWittek$b Peter$01017929 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996426328203316 996 $aQuantum machine learning$92390978 997 $aUNISA