Cargando…
Quantum machine learning : what quantum computing means to data mining /
"Quantum 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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Diego, CA :
Academic Press, an imprint of Elsevier,
2014.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.