Wittek, Peter creator author. text Electronic books. cau 2014 1st ed. monographic eng

electronic resource

1 online resource (176 pages) Bridging 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. -- Front 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. 2.7. 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. Chapter 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. 7.7. 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. 10.5. 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. Peter Wittek. Quantum theory Data mining SCIENCE Energy SCIENCE Mechanics General SCIENCE Physics General Data mining Quantum theory Maschinelles Lernen Quanteninformatik Data Mining QC174.12 530.12 Wittek, Peter author. [San Diego, CA] : Academic Press, 2014 9780128010990 0128010991 132211434X 9781322114347 http://www.sciencedirect.com/science/book/9780128009536 http://www.sciencedirect.com/science/book/9780128009536 IDEBK 140303 20190328114808.0 ocn890854311 eng