| Edition |
First edition. |
| Description |
1 online resource (176 pages) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Print version record. |
| Contents |
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. |
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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. |
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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. |
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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. |
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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. |
| Summary |
"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 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 Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research." |
| Subject |
Quantum theory.
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Data mining.
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Théorie quantique.
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Exploration de données (Informatique)
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SCIENCE -- Energy.
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SCIENCE -- Mechanics -- General.
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SCIENCE -- Physics -- General.
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Data mining
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Quantum theory
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Maschinelles Lernen
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Quanteninformatik
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Data Mining
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| Other Form: |
Print version: Wittek, Peter author. Quantum Machine Learning. [San Diego, CA] : Academic Press, 2014 9780128009536 |
| ISBN |
9780128010990 (electronic bk.) |
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0128010991 (electronic bk.) |
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132211434X (electronic bk.) |
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9781322114347 (electronic bk.) |
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9780128009536 |
| Standard No. |
CHDSB 006318022 |
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DEBBG BV043216138 |
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DEBSZ 434139475 |
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AU@ 000054998574 |
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