23 Temmuz 2023 Ön baskı Açık Erişim

Korkmaz, Ufuk; Türkpençe, Deniz

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/263305</identifier>
  <creators>
    <creator>
      <creatorName>Korkmaz, Ufuk</creatorName>
      <givenName>Ufuk</givenName>
      <familyName>Korkmaz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5836-5262</nameIdentifier>
      <affiliation>İstanbul Teknik Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Türkpençe, Deniz</creatorName>
      <givenName>Deniz</givenName>
      <familyName>Türkpençe</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5182-374X</nameIdentifier>
      <affiliation>İstanbul Teknik Üniversitesi</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Dissipative Learning Of A Quantum Classifier</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>Collision model</subject>
    <subject>Information reservoir</subject>
    <subject>quantum learning</subject>
    <subject>cost function</subject>
    <subject>training</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-07-23</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="doi">10.1007/s12043-023-02653-7</alternateIdentifier>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/263305</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.48550/arXiv.2307.12293</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by-sa">Creative Commons Attribution Share-Alike</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyze the learning dynamics of a quantum classifier model that works as an open quantum system which is an alternative to the standard quantum circuit model. According to the obtained results, the model can be successfully trained with a gradient descent (GD) based algorithm. The fact that these optimization processes have been obtained with continuous dynamics, shows promise for the development of a differentiable activation function for the classifier model.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier>
      <awardNumber>120F353</awardNumber>
    </fundingReference>
  </fundingReferences>
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