Title
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
Quantum Robotics
A Primer on Current Science and Future Perspectives
Prateek Tandon
Stanley Lam
Ben Shih
Tanay Mehta
Alex Mitev
Zhiyang Ong
Quantum Robotics Group
SYNTHESIS LECTURES ON QUANTUM COMPUTING #10
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