LEADER 07399nam 2200697 450 001 9910160669803321 005 20170125162047.0 010 $a1-62705-995-4 024 7 $a10.2200/S00746ED1V01Y201612QMC010 035 $a(CKB)3710000001022333 035 $a(MiAaPQ)EBC4789115 035 $a(CaBNVSL)swl00407061 035 $a(OCoLC)970004428 035 $a(IEEE)7833479 035 $a(MOCL)201612QMC010 035 $a(EXLCZ)993710000001022333 100 $a20170124d2017 fy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aQuantum robotics $ea primer on current science and future perspectives /$fPrateek Tandon, Stanley Lam, Ben Shih, Tanay Mehta, Alex Mitev, Zhiyang Ong 210 1$a[San Rafael, California] :$cMorgan & Claypool,$d2017. 215 $a1 online resource (151 pages) $ccolor illustrations 225 1 $aSynthesis lectures on quantum computing,$x1945-9734 ;$v# 10 300 $aPart of: Synthesis digital library of engineering and computer science. 311 $a1-62705-913-X 320 $aIncludes bibliographical references (pages 107-127) and index. 327 $a1. Introduction -- 1.1 What does quantum robotics study? -- 1.2 Aim and overview of our work -- 1.3 Quantum operating principles -- 327 $a2. Relevant background on quantum mechanics -- 2.1 Qubits and superposition -- 2.2 Quantum states and entanglement -- 2.3 Schro?dinger 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 -- 327 $a3. 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 -- 327 $a4. 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 -- 327 $a5. 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 -- 327 $a6. 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 -- 327 $a7. 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 -- 327 $a8. Conclusion -- A. Cheatsheet of quantum concepts discussed -- Bibliography -- Authors' biographies -- Index. 330 3 $aQuantum robotics is an emerging engineering and scientific research discipline that explores the application of quantum mechanics, quantum computing, quantum algorithms, and related fields to robotics. This work broadly surveys advances in our scientific understanding and engineering of quantum mechanisms and how these developments are expected to impact the technical capability for robots to sense, plan, learn, and act in a dynamic environment. It also discusses the new technological potential that quantum approaches may unlock for sensing and control, especially for exploring and manipulating quantum-scale environments. Finally, the work surveys the state of the art in current implementations, along with their benefits and limitations, and provides a roadmap for the future. 410 0$aSynthesis digital library of engineering and computer science. 410 0$aSynthesis lectures on quantum computing ;$v# 10.$x1945-9734 606 $aRobotics 606 $aQuantum theory 610 $aQuantum Robotics 610 $aQuantum Computing 610 $aQuantum Algorithms 615 0$aRobotics. 615 0$aQuantum theory. 676 $a629.892 700 $aTandon$b Prateek$01159329 702 $aStanley Lam 702 $aShih$b Ben 702 $aMehta$b Tanay 702 $aMitev$b Alex 702 $aOng$b Zhiyang 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910160669803321 996 $aQuantum robotics$92962335 997 $aUNINA