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035   $a(DE-He213)978-3-319-96424-9
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100   $a20180830d2018      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSupervised Learning with Quantum Computers$b[electronic resource] /$fby Maria Schuld, Francesco Petruccione
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (293 pages)
225 1 $aQuantum Science and Technology,$x2364-9054
311 1 $a3-319-96423-2 
327   $aIntroduction -- Background -- How quantum computers can classify data -- Organisation of the book -- Machine Learning -- Prediction -- Models -- Training -- Methods in machine learning -- Quantum Information -- Introduction to quantum theory -- Introduction to quantum computing -- An example: The Deutsch-Josza algorithm -- Strategies of information encoding -- Important quantum routines -- Quantum advantages -- Computational complexity of learning -- Sample complexity -- Model complexity -- Information encoding -- Basis encoding -- Amplitude encoding -- Qsample encoding -- Hamiltonian encoding -- Quantum computing for inference -- Linear models -- Kernel methods -- Probabilistic models -- Quantum computing for training -- Quantum blas -- Search and amplitude amplification -- Hybrid training for variational algorithms -- Quantum adiabatic machine learning -- Learning with quantum models -- Quantum extensions of Ising-type models -- Variational classifiers and neural networks -- Other approaches to build quantum models -- Prospects for near-term quantum machine learning -- Small versus big data -- Hybrid versus fully coherent approaches -- Qualitative versus quantitative advantages -- What machine learning can do for quantum computing -- References.
330   $aQuantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
410  0$aQuantum Science and Technology,$x2364-9054
606   $aQuantum physics
606   $aQuantum computers
606   $aPattern recognition
606   $aSpintronics
606   $aPhysics
606   $aArtificial intelligence
615  0$aQuantum physics.
615  0$aQuantum computers.
615  0$aPattern recognition.
615  0$aSpintronics.
615  0$aPhysics.
615  0$aArtificial intelligence.
676   $a530.1201514
700   $aSchuld$b Maria$4aut$4http://id.loc.gov/vocabulary/relators/aut$0878448
702   $aPetruccione$b Francesco$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910300533803321
996   $aSupervised Learning with Quantum Computers$92533821
997   $aUNINA