LEADER 02771nam 2200757 a 450 001 996247864003316 005 20221108053514.0 010 $a0-8047-1457-6 024 7 $a2027/heb02793 035 $a(CKB)1000000000396798 035 $a(dli)HEB02793 035 $a(SSID)ssj0000084946 035 $a(PQKBManifestationID)11116118 035 $a(PQKBTitleCode)TC0000084946 035 $a(PQKBWorkID)10006776 035 $a(PQKB)10726616 035 $a(MiU)MIU01000000000000005398406 035 $a(EXLCZ)991000000000396798 100 $a19880113d1988 uy 0 101 0 $aeng 135 $aurmnummmmuuuu 181 $ctxt 182 $cc 183 $acr 200 10$aTo love, honor, and obey in colonial Mexico $econflicts over marriage choice, 1574-1821 /$fPatricia Seed 210 $aStanford, Calif. $cStanford University Press$dc1988 215 $a1 online resource (viii, 322 p. ) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-8047-2159-9 320 $aIncludes bibliographical references and index. 410 0$aACLS Fellows? publications. 410 0$aACLS Humanities E-Book. 531 $aTO LOVE, HONOR, AND OBEY IN COLONIAL MEXICO 531 $aTO LOVE, HONOR, & OBEY IN COLONIAL MEXICO: CONFLICTS OVER MARRIAGE CHOICE, 1574-1821 606 $aMarriage$zMexico$zMexico City$xHistory 606 $aMarriage$xParental consent$zMexico$zMexico City$xHistory 606 $aMarriage$xReligious aspects$xCatholic Church 606 $aMate selection$zMexico$zMexico City$xHistory 606 $aMarriage$xHistory$zMexico City$zMexico 606 $aMarriage$xParental consent$xHistory$zMexico City$zMexico 606 $aMarriage$xReligious aspects$xCatholic Church$zMexico$zMexico City 606 $aMate selection$xHistory 606 $aSociology & Social History$2HILCC 606 $aSocial Sciences$2HILCC 606 $aFamily & Marriage$2HILCC 607 $aMexico$xSocial conditions$yTo 1810 615 0$aMarriage$xHistory. 615 0$aMarriage$xParental consent$xHistory. 615 0$aMarriage$xReligious aspects$xCatholic Church. 615 0$aMate selection$xHistory. 615 0$aMarriage$xHistory 615 0$aMarriage$xParental consent$xHistory 615 0$aMarriage$xReligious aspects$xCatholic Church 615 0$aMate selection$xHistory 615 7$aSociology & Social History 615 7$aSocial Sciences 615 7$aFamily & Marriage 676 $a306.8/1/097253 700 $aSeed$b Patricia$0240961 712 02$aAmerican Council of Learned Societies. 801 0$bMiU 801 1$bMiU 906 $aBOOK 912 $a996247864003316 996 $aTo love, honor, and obey in colonial Mexico$9277320 997 $aUNISA 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