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 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

Table of Contents

Chapter 1. Introduction
Chapter 2. Machine Learning
Chapter 3. Quantum Mechanics
Chapter 4. Quantum Computing
Chapter 5. Unsupervised Learning
Chapter 6. Pattern Recognition and Neural Networks
Chapter 7. Supervised Learning and Support Vector Machines
Chapter 8. Regression Analysis
Chapter 9. Boosting
Chapter 10. Clustering Structure and Quantum Computing
Chapter 11. Quantum Pattern Recognition
Chapter 12. Quantum Classification
Chapter 13. Quantum Process Tomography and Regression
Chapter 14. Boosting and Adiabatic Quantum Computing