24 Temmuz 2023 Ön baskı Açık Erişim

Aygül, Ekin Erdem; Topal, Melih Can; Korkmaz, Ufuk; Türkpençe, Deniz

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/263307</identifier>
  <creators>
    <creator>
      <creatorName>Aygül, Ekin Erdem</creatorName>
      <givenName>Ekin Erdem</givenName>
      <familyName>Aygül</familyName>
      <affiliation>İstanbul Teknik Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Topal, Melih Can</creatorName>
      <givenName>Melih Can</givenName>
      <familyName>Topal</familyName>
      <affiliation>İstanbul Teknik Üniversitesi</affiliation>
    </creator>
    <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>Application Of Power Flow Problem To An Open Quantum Neural Hardware</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>quantum neuron</subject>
    <subject>information reservoir</subject>
    <subject>collisional model</subject>
    <subject>training and learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-07-24</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/263307</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.48550/arXiv.2307.12678</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;Significant progress in the construction of physical hardware for quantum computers has necessitated the development of new algorithms or protocols for the application of real-world problems on quantum computers. One of these problems is the power flow problem, which helps us understand the generation, distribution, and consumption of electricity in a system. In this study, the solution of a balanced 4-bus power system supported by the Newton-Raphson method is investigated using a newly developed dissipative quantum neural network hardware. This study presents the findings on how the proposed quantum network can be applied to the relevant problem and how the solution performance varies depending on the network parameters.&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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