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Record Nr. |
UNINA9910160669803321 |
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Autore |
Tandon Prateek |
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Titolo |
Quantum robotics : a primer on current science and future perspectives / / Prateek Tandon, Stanley Lam, Ben Shih, Tanay Mehta, Alex Mitev, Zhiyang Ong |
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Pubbl/distr/stampa |
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[San Rafael, California] : , : Morgan & Claypool, , 2017 |
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ISBN |
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Descrizione fisica |
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1 online resource (151 pages) : color illustrations |
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Collana |
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Synthesis lectures on quantum computing, , 1945-9734 ; ; # 10 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Part of: Synthesis digital library of engineering and computer science. |
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Nota di bibliografia |
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Includes bibliographical references (pages 107-127) and index. |
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Nota di contenuto |
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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 |
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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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Sommario/riassunto |
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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 |
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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. |
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