<?xml version="1.0"?><rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:edm="http://www.europeana.eu/schemas/edm/" xmlns:wgs84_pos="http://www.w3.org/2003/01/geo/wgs84_pos" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:rdaGr2="http://rdvocab.info/ElementsGr2" xmlns:oai="http://www.openarchives.org/OAI/2.0/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:ore="http://www.openarchives.org/ore/terms/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:dcterms="http://purl.org/dc/terms/"><edm:WebResource rdf:about="http://www.dlib.si/stream/URN:NBN:SI:doc-DIS2VUE7/dfa4025-3973ee83217d134-e-4d7b8c93a-/PDF"><dcterms:extent>1964 KB</dcterms:extent></edm:WebResource><edm:WebResource rdf:about="http://www.dlib.si/stream/URN:NBN:SI:doc-DIS2VUE7/ff8c78a1-f88f-4d84-ad53-598ce13c6366/TEXT"><dcterms:extent>27 KB</dcterms:extent></edm:WebResource><edm:TimeSpan rdf:about="2014-2024"><edm:begin xml:lang="en">2014</edm:begin><edm:end xml:lang="en">2024</edm:end></edm:TimeSpan><edm:ProvidedCHO rdf:about="URN:NBN:SI:doc-DIS2VUE7"><dcterms:isPartOf rdf:resource="https://www.dlib.si/details/URN:NBN:SI:spr-OE00UKYR" /><dcterms:issued>2019</dcterms:issued><dc:creator>Rot, Miha</dc:creator><dc:format xml:lang="sl">številka:2</dc:format><dc:format xml:lang="sl">letnik:6</dc:format><dc:format xml:lang="sl">9 str.</dc:format><dc:identifier>ISSN:2385-8567</dc:identifier><dc:identifier>COBISSID_HOST:3353700</dc:identifier><dc:identifier>URN:URN:NBN:SI:doc-DIS2VUE7</dc:identifier><dc:language>en</dc:language><dc:publisher xml:lang="sl">Založba Fakultete za matematiko in fiziko Univerze v Ljubljani</dc:publisher><dcterms:isPartOf xml:lang="sl">Matrika</dcterms:isPartOf><dc:subject xml:lang="sl">kvantna mehanika</dc:subject><dc:subject xml:lang="en">machine learning</dc:subject><dc:subject xml:lang="en">quantum mechanics</dc:subject><dc:subject xml:lang="sl">strojno učenje</dc:subject><dcterms:temporal rdf:resource="2014-2024" /><dc:title xml:lang="sl">Quantum machine learning|</dc:title><dc:description xml:lang="sl">Advances in processing power and algorithms have made machine learning a very potent tool. This article attempts to introduce machine learning and present two applications of machine learning in quantum physics. The first example deals with the ground state energy of an electron in 2D potential, while the second one touches upon the applicability of machine learning to the quantum many-body problems. The article concludes with a brief description of potential machine learning speed-ups promised by quantum computers</dc:description><dc:description xml:lang="sl">Zaradi napredkov v algoritmih in procesorski moči je postalo strojno učenje zelo močno orodje. Članek se začne s kratko predstavitvijo strojnega učenja, nato predstavi dva primera uporabe strojnega učenja v kvantni fiziki in zaključi z opisom potencialnih pohitritev, ki jih strojnemu učenju obljubljajo kvantni računalniki. Prvi primer obravnava metodo za določanje osnovnega stanja elektrona v 2D potencialu, drugi pa predstavi uporabo strojnega učenja pri opisu kvantnih mnogodelčnih sistemov</dc:description><edm:type>TEXT</edm:type><dc:type xml:lang="sl">znanstveno časopisje</dc:type><dc:type xml:lang="en">journals</dc:type><dc:type rdf:resource="http://www.wikidata.org/entity/Q361785" /></edm:ProvidedCHO><ore:Aggregation rdf:about="http://www.dlib.si/?URN=URN:NBN:SI:doc-DIS2VUE7"><edm:aggregatedCHO rdf:resource="URN:NBN:SI:doc-DIS2VUE7" /><edm:isShownBy rdf:resource="http://www.dlib.si/stream/URN:NBN:SI:doc-DIS2VUE7/dfa4025-3973ee83217d134-e-4d7b8c93a-/PDF" /><edm:rights rdf:resource="http://rightsstatements.org/vocab/InC/1.0/" /><edm:provider>Slovenian National E-content Aggregator</edm:provider><edm:dataProvider xml:lang="en">National and University Library of Slovenia</edm:dataProvider><edm:object rdf:resource="http://www.dlib.si/streamdb/URN:NBN:SI:doc-DIS2VUE7/maxi/edm" /><edm:isShownAt rdf:resource="http://www.dlib.si/details/URN:NBN:SI:doc-DIS2VUE7" /></ore:Aggregation></rdf:RDF>