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Full Description
This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.
In summary, the book:
Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines.
Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks.
Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting.
Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost.
Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.
This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.
Contents
Part I: Introduction to Statistical & Quantum Learning 1: Fundamentals of Statistics 2: Fundamentals of Quantum Machines Part II: Introduction to Quantum Machine Learning 3: Machine Learning with Supervised Quantum Models 4: Machine Learning with Unsupervised Quantum Models 5: Artificial Neural Networks Part III: Quantum Models 6: Quantum Information Science: Bridging the Gap between the Classical and Quantum Worlds 7: Quantum Machine Learning Approaches 8: Quantum Classification 9: Boosting in QMLPart IV: Quantum Evaluation Models 10: Deep Quantum Learning 11: Ensembles and QBoost 12: Quantum Process Tomography and Regression
