LEADER 01106nam0 2200289 i 450 001 SUN0068176 005 20140707014221.422 010 $a03-04-29723-2 100 $a20090317d1976 |0engc50 ba 101 $aeng 102 $aGB 105 $a||||E ||||| 200 1 $aCassell's new spelling dictionary$fcompiled by L.B. Firnberg and D. Firnberg 210 $aLondon$cCassell$d1976 215 $a[6], 250 p.$d21 cm. 606 $aDizionari$2SG$3SUNC029823 620 $aGB$dLondon$3SUNL000015 676 $a428.1$v21 702 1$aFirnberg$b, Leopold B.$3SUNV053980 702 1$aFirnberg$b, David$3SUNV053981 712 $aCassell$3SUNV001893$4650 801 $aIT$bSOL$c20181109$gRICA 912 $aSUN0068176 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE POLITICHE JEAN MONNET$d04 CONS Dizionari 1.6 $e04 OM 556 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE POLITICHE JEAN MONNET$gOM$h556$kCONS Dizionari 1.6$oc$qa 996 $aCassell's new spelling dictionary$91414103 997 $aUNICAMPANIA LEADER 04395nam 22006975 450 001 9910300533803321 005 20251113194454.0 010 $a3-319-96424-0 024 7 $a10.1007/978-3-319-96424-9 035 $a(CKB)4100000006098318 035 $a(MiAaPQ)EBC5504971 035 $a(DE-He213)978-3-319-96424-9 035 $a(PPN)229917828 035 $a(MiAaPQ)EBC29095525 035 $a(EXLCZ)994100000006098318 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 /$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 $aQuantum Science and Technology,$x2364-9062 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 buildquantum 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,$x2364-9062 606 $aQuantum physics 606 $aQuantum computers 606 $aPattern recognition systems 606 $aSpintronics 606 $aMathematical physics 606 $aArtificial intelligence 606 $aQuantum Physics 606 $aQuantum Computing 606 $aAutomated Pattern Recognition 606 $aSpintronics 606 $aTheoretical, Mathematical and Computational Physics 606 $aArtificial Intelligence 615 0$aQuantum physics. 615 0$aQuantum computers. 615 0$aPattern recognition systems. 615 0$aSpintronics. 615 0$aMathematical physics. 615 0$aArtificial intelligence. 615 14$aQuantum Physics. 615 24$aQuantum Computing. 615 24$aAutomated Pattern Recognition. 615 24$aSpintronics. 615 24$aTheoretical, Mathematical and Computational Physics. 615 24$aArtificial Intelligence. 676 $a530.1201514 700 $aSchuld$b Maria$4aut$4http://id.loc.gov/vocabulary/relators/aut$0878448 702 $aPetruccione$b F$g(Francesco),$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910300533803321 996 $aSupervised Learning with Quantum Computers$92533821 997 $aUNINA