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Quantum Robotics

Author Zhiyang Ong , Alex Mitev , Tanay Mehta , Ben Shih , Stanley Lam , Prateek Tandon
Release Date: 2017/01/01
ISBN: 9781627056854
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Book Description

Quantum robotics is an emerging engineering and scientific research discipline that explores the application of quantum mechanics, quantum computing, quantum algorithms, and related fields to robotics. This work broadly surveys advances in our scientific understanding and engineering of quantum mechanisms and how these developments are expected to impact the technical capability for robots to sense, plan, learn, and act in a dynamic environment. It also discusses the new technological potential that quantum approaches may unlock for sensing and control, especially for exploring and manipulating quantum-scale environments. Finally, the work surveys the state of the art in current implementations, along with their benefits and limitations, and provides a roadmap for the future.

Table of Contents

  1. Cover
  2. Half title
  3. Copyright
  4. Title
  5. Abstract
  6. Contents
  7. Preface
  8. Acknowledgments
  9. Notation
  10. 1 Introduction
    1. 1.1 What does Quantum Robotics Study?
    2. 1.2 Aim and Overview of our Work
    3. 1.3 Quantum Operating Principles
  11. 2 Relevant Background on Quantum Mechanics
    1. 2.1 Qubits and Superposition
    2. 2.2 Quantum States and Entanglement
    3. 2.3 Schrödinger Equation and Quantum State Evolution
    4. 2.4 Quantum Logic Gates and Circuits
      1. 2.4.1 Reversible Computing and Landauer’s Principle
      2. 2.4.2 Notable Quantum Gates
      3. 2.4.3 Quantum Circuit for Fast Fourier Transform
    5. 2.5 Quantum Computing Mechanisms
      1. 2.5.1 Quantum Parallelism
      2. 2.5.2 Challenges with Quantum Parallelism
      3. 2.5.3 Grover’s Search Algorithm
      4. 2.5.4 Adiabatic Quantum Optimization
      5. 2.5.5 Adiabatic Hardware and Speedups
      6. 2.5.6 Shor’s Quantum Factorization Algorithm
      7. 2.5.7 Quantum Teleportation
    6. 2.6 Quantum Operating Principles (QOPs) Summary
    7. 2.7 Chapter Summary
  12. 3 Quantum Search
    1. 3.1 Uninformed Grover Tree Search
    2. 3.2 Informed Quantum Tree Search
    3. 3.3 Application of Quantum Annealing to STRIPS Classical Planning
      1. 3.3.1 Classical STRIPS Planning
      2. 3.3.2 Application of Quantum Annealing to STRIPS Planning
    4. 3.4 Chapter Summary
  13. 4 Quantum Agent Models
    1. 4.1 Classical Markov Decision Processes
    2. 4.2 Classical Partially Observable Markov Decision Processes
    3. 4.3 Quantum Superoperators
    4. 4.4 Quantum MDPs
    5. 4.5 QOMDPs
    6. 4.6 Classical Reinforcement Learning Models
      1. 4.6.1 Projection Simulation Agents
      2. 4.6.2 Reflective Projection Simulation Agents
    7. 4.7 Quantum Agent Learning
    8. 4.8 Multi-armed Bandit Problem and Single Photon Decision Maker
    9. 4.9 Chapter Summary
  14. 5 Machine Learning Mechanisms for Quantum Robotics
    1. 5.1 Quantum Operating Principles in Quantum Machine Learning
      1. 5.1.1 Quantum Memory
      2. 5.1.2 Quantum Inner Products and Distances
      3. 5.1.3 Hamiltonian Simulation
      4. 5.1.4 QOPs Summary for Quantum Machine Learning
    2. 5.2 Quantum Principal Component Analysis (PCA)
      1. 5.2.1 Classical PCA Analysis
      2. 5.2.2 Quantum PCA Analysis
      3. 5.2.3 Potential Impact of Quantum PCA on Robotics
    3. 5.3 Quantum Regression
      1. 5.3.1 Least Squares Fitting
      2. 5.3.2 Quantum Approaches to Curve Fitting
      3. 5.3.3 Potential Impact of Quantum Regression on Robotics
    4. 5.4 Quantum Clustering
      1. 5.4.1 Classical Cluster Analysis
      2. 5.4.2 Quantum Cluster Analysis
      3. 5.4.3 Potential Impact of Quantum Clustering on Robotics
    5. 5.5 Quantum Support Vector Machines
      1. 5.5.1 Classical SVM Analysis
      2. 5.5.2 Quantum SVM Analysis
      3. 5.5.3 Potential Impact of Quantum SVMs on Robotics
    6. 5.6 Quantum Bayesian Networks
      1. 5.6.1 Classical Bayesian Network Structure Learning
      2. 5.6.2 Bayesian Network Structure Learning using Adiabatic Optimization
      3. 5.6.3 Potential Impact of Quantum Bayesian Networks on Robotics
    7. 5.7 Quantum Artificial Neural Networks
      1. 5.7.1 Classical Artificial Neural Networks
      2. 5.7.2 Quantum Approaches to Artificial Neural Networks
      3. 5.7.3 Potential Impact of Quantum Artificial Neural Networks to Robotics
    8. 5.8 Manifold Learning and Quantum Speedups
      1. 5.8.1 Classical Manifold Learning
      2. 5.8.2 Quantum Speedups for Manifold Learning
      3. 5.8.3 Potential Impact of Quantum Manifold Learning on Robotics
    9. 5.9 Quantum Boosting
      1. 5.9.1 Classical Boosting Analysis
      2. 5.9.2 QBoost
      3. 5.9.3 Potential Impact of Quantum Boosting on Robotics
    10. 5.10 Chapter Summary
  15. 6 Quantum Filtering and Control
    1. 6.1 Quantum Measurements
      1. 6.1.1 Projective Measurements
      2. 6.1.2 Continuous Measurements
    2. 6.2 Hidden Markov Models and Quantum Extension
      1. 6.2.1 Classical Hidden Markov Models
      2. 6.2.2 Hidden Quantum Markov Models
    3. 6.3 Kalman Filtering and Quantum Extension
      1. 6.3.1 Classic Kalman Filtering
      2. 6.3.2 Quantum Kalman Filtering
    4. 6.4 Classical and Quantum Control
      1. 6.4.1 Overview of Classical Control
      2. 6.4.2 Overview of Quantum Control Models
      3. 6.4.3 Bilinear Models (BLM)
      4. 6.4.4 Markovian Master Equation (MME)
      5. 6.4.5 Stochastic Master Equation (SME)
      6. 6.4.6 Linear Quantum Stochastic Differential Equation (LQSDE)
      7. 6.4.7 Verification of Quantum Control Algorithms
    5. 6.5 Chapter Summary
  16. 7 Current Strategies for Quantum Implementation
    1. 7.1 DiVincenzo Definition
    2. 7.2 Mosca Classification
    3. 7.3 Comparison of DiVincenzo and Mosca Approaches
    4. 7.4 Quantum Computing Physical Implementations
    5. 7.5 Case Study Evaluation of D-Wave Machine
    6. 7.6 Toward General Purpose Quantum Computing and Robotics
    7. 7.7 Chapter Summary
  17. 8 Conclusion
  18. A Cheatsheet of Quantum Concepts Discussed
  19. Bibliography
  20. Authors’ Biographies
  21. Index
18.116.15.161