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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
1.1 What does Quantum Robotics Study?
1.2 Aim and Overview of our Work
1.3 Quantum Operating Principles
2 Relevant Background on Quantum Mechanics
2.1 Qubits and Superposition
2.2 Quantum States and Entanglement
2.3 Schrödinger Equation and Quantum State Evolution
2.4 Quantum Logic Gates and Circuits
2.4.1 Reversible Computing and Landauer’s Principle
2.4.2 Notable Quantum Gates
2.4.3 Quantum Circuit for Fast Fourier Transform
2.5 Quantum Computing Mechanisms
2.5.1 Quantum Parallelism
2.5.2 Challenges with Quantum Parallelism
2.5.3 Grover’s Search Algorithm
2.5.4 Adiabatic Quantum Optimization
2.5.5 Adiabatic Hardware and Speedups
2.5.6 Shor’s Quantum Factorization Algorithm
2.5.7 Quantum Teleportation
2.6 Quantum Operating Principles (QOPs) Summary
2.7 Chapter Summary
3 Quantum Search
3.1 Uninformed Grover Tree Search
3.2 Informed Quantum Tree Search
3.3 Application of Quantum Annealing to STRIPS Classical Planning
3.3.1 Classical STRIPS Planning
3.3.2 Application of Quantum Annealing to STRIPS Planning
3.4 Chapter Summary
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
4.6.1 Projection Simulation Agents
4.6.2 Reflective Projection Simulation Agents
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
5.1.1 Quantum Memory
5.1.2 Quantum Inner Products and Distances
5.1.3 Hamiltonian Simulation
5.1.4 QOPs Summary for Quantum Machine Learning
5.2 Quantum Principal Component Analysis (PCA)
5.2.1 Classical PCA Analysis
5.2.2 Quantum PCA Analysis
5.2.3 Potential Impact of Quantum PCA on Robotics
5.3 Quantum Regression
5.3.1 Least Squares Fitting
5.3.2 Quantum Approaches to Curve Fitting
5.3.3 Potential Impact of Quantum Regression on Robotics
5.4 Quantum Clustering
5.4.1 Classical Cluster Analysis
5.4.2 Quantum Cluster Analysis
5.4.3 Potential Impact of Quantum Clustering on Robotics
5.5 Quantum Support Vector Machines
5.5.1 Classical SVM Analysis
5.5.2 Quantum SVM Analysis
5.5.3 Potential Impact of Quantum SVMs on Robotics
5.6 Quantum Bayesian Networks
5.6.1 Classical Bayesian Network Structure Learning
5.6.2 Bayesian Network Structure Learning using Adiabatic Optimization
5.6.3 Potential Impact of Quantum Bayesian Networks on Robotics
5.7 Quantum Artificial Neural Networks
5.7.1 Classical Artificial Neural Networks
5.7.2 Quantum Approaches to Artificial Neural Networks
5.7.3 Potential Impact of Quantum Artificial Neural Networks to Robotics
5.8 Manifold Learning and Quantum Speedups
5.8.1 Classical Manifold Learning
5.8.2 Quantum Speedups for Manifold Learning
5.8.3 Potential Impact of Quantum Manifold Learning on Robotics
5.9 Quantum Boosting
5.9.1 Classical Boosting Analysis
5.9.2 QBoost
5.9.3 Potential Impact of Quantum Boosting on Robotics
5.10 Chapter Summary
6 Quantum Filtering and Control
6.1 Quantum Measurements
6.1.1 Projective Measurements
6.1.2 Continuous Measurements
6.2 Hidden Markov Models and Quantum Extension
6.2.1 Classical Hidden Markov Models
6.2.2 Hidden Quantum Markov Models
6.3 Kalman Filtering and Quantum Extension
6.3.1 Classic Kalman Filtering
6.3.2 Quantum Kalman Filtering
6.4 Classical and Quantum Control
6.4.1 Overview of Classical Control
6.4.2 Overview of Quantum Control Models
6.4.3 Bilinear Models (BLM)
6.4.4 Markovian Master Equation (MME)
6.4.5 Stochastic Master Equation (SME)
6.4.6 Linear Quantum Stochastic Differential Equation (LQSDE)
6.4.7 Verification of Quantum Control Algorithms
6.5 Chapter Summary
7 Current Strategies for Quantum Implementation
7.1 DiVincenzo Definition
7.2 Mosca Classification
7.3 Comparison of DiVincenzo and Mosca Approaches
7.4 Quantum Computing Physical Implementations
7.5 Case Study Evaluation of D-Wave Machine
7.6 Toward General Purpose Quantum Computing and Robotics
7.7 Chapter Summary
8 Conclusion
A Cheatsheet of Quantum Concepts Discussed
Bibliography
Authors’ Biographies
Index
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Quantum Robotics
Quantum Robotics
A Primer on Current Science and Future Perspectives
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