18 Temmuz 2023 Ön baskı Açık Erişim

Korkmaz, Ufuk; Türkpençe, Deniz

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/263301</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>Information-Driven Nonlinear Quantum Neuron</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>Quantum neural networks</subject>
    <subject>quantum neuron</subject>
    <subject>quantum learning</subject>
    <subject>open quantum system</subject>
    <subject>cost function</subject>
    <subject>quantum  activation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-07-18</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/263301</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.48550/arXiv.2307.09017</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 promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circuit model, and implementing them based on hardware. However, the ability to capture the non-linear behavior in neural networks using a computation process that usually involves linear quantum mechanics principles remains a major challenge in both categories. In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed, which presents non-linear behaviour. The model&amp;#39;s compatibility with learning processes is tested through the obtained analytical results. In other words, we show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.&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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