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Full Description
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.
Contents
IntroductionChapter 1: Machine LearningChapter 2: Quantum MechanicsChapter 3: Quantum ComputingChapter 4: Unsupervised LearningChapter 5: Pattern Recognition and Neural NetworksChapter 6: Supervised Learning and SUpport Vector MachinesChapter 7: Regression AnalysisChapter 8: BoostingChapter 9: Clustering Structure and Quantum ComputingChapter 10: Quantum Pattern RecognitionChapter 11: Quantum ClassificationChapter 12: Quantum Process TomographyChapter 13: Boosting and Adiabatic Quantum Computing
