Quantum machine learning : what quantum computing means to data mining - Wittek, Peter (San Diego, CA : Academic Press, an imprint of Elsevier, 2014)
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. --
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.
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Keywords: Quantum theory; Data mining; SCIENCE -- Energy; SCIENCE -- Mechanics -- General; SCIENCE -- Physics -- General; Data mining; Quantum theory; Maschinelles Lernen; Quanteninformatik; Data Mining; Electronic books