Acknowledgments
by Zhiyang Ong, Alex Mitev, Tanay Mehta, Ben Shih, Stanley Lam, Prateek Tandon
Quantum Robotics
- Cover
- Half title
- Copyright
- Title
- Abstract
- Contents
- Preface
- Acknowledgments
- Notation
- 1 Introduction
- 2 Relevant Background on Quantum Mechanics
- 3 Quantum Search
- 4 Quantum Agent Models
- 4.1 Classical Markov Decision Processes
- 4.2 Classical Partially Observable Markov Decision Processes
- 4.3 Quantum Superoperators
- 4.4 Quantum MDPs
- 4.5 QOMDPs
- 4.6 Classical Reinforcement Learning Models
- 4.7 Quantum Agent Learning
- 4.8 Multi-armed Bandit Problem and Single Photon Decision Maker
- 4.9 Chapter Summary
- 5 Machine Learning Mechanisms for Quantum Robotics
- 5.1 Quantum Operating Principles in Quantum Machine Learning
- 5.2 Quantum Principal Component Analysis (PCA)
- 5.3 Quantum Regression
- 5.4 Quantum Clustering
- 5.5 Quantum Support Vector Machines
- 5.6 Quantum Bayesian Networks
- 5.7 Quantum Artificial Neural Networks
- 5.8 Manifold Learning and Quantum Speedups
- 5.9 Quantum Boosting
- 5.10 Chapter Summary
- 6 Quantum Filtering and Control
- 7 Current Strategies for Quantum Implementation
- 8 Conclusion
- A Cheatsheet of Quantum Concepts Discussed
- Bibliography
- Authors’ Biographies
- Index
Acknowledgments
We would like to acknowledge Dr. Steven Adachi, Professor Marco Lanzagorta, Professor Jeffrey Uhlmann, and Dr. Henry Yuen, among others, for their helpful comments in the development of this work.
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