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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:language>eng</dc:language><dc:creator>Sancho Lorente, Teresa</dc:creator><dc:creator>Zueco Láinez, David</dc:creator><dc:title>Clasificación de transiciones de fase cuánticas mediante aprendizaje automático cuántico.</dc:title><dc:identifier>TAZ-TFG-2021-3093</dc:identifier><dc:description>In this dissertation we discuss an example of quantum computing supremacy based on classification of quantum phase transitions. To do so, we focus our attention in the quantum Ising model and present the results of characterizing its phase transition making use of a machine learning algorithm used to do classification tasks and known as Support Vector Machines. &lt;br /&gt;&lt;br /&gt;</dc:description><dc:publisher>Universidad de Zaragoza</dc:publisher><dc:date>2021</dc:date><dc:source>http://zaguan.unizar.es/record/110301</dc:source><dc:identifier>http://zaguan.unizar.es/record/110301</dc:identifier><dc:identifier>oai:zaguan.unizar.es:110301</dc:identifier></dc:dc>

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