cff-version: 1.2.0
abstract: "<p>This data is associated with the paper "Surrogate-guided Optimization in Quantum Networks".</p><p>In this work we introduce an efficient optimization workflow using machine-learning models that outperforms traditional techniques, addressing the challenges of complex, computationally demanding simulations in quantum networking. Please find guidelines and more context in REAMDE.md file.</p>"
authors:
  - family-names: Prielinger
    given-names: Luise
    orcid: "https://orcid.org/0009-0004-2348-501X"
  - family-names: Gómez Iñesta
    given-names: Álvaro
    orcid: "https://orcid.org/0000-0001-7425-065X"
  - family-names: Vardoyan
    given-names: Gayane
title: "Data underlying the publication: Surrogate-guided Optimization in Quantum Networks"
keywords:
version: 2
identifiers:
  - type: doi
    value: 10.4121/a07a9e97-f34c-4e7f-9f68-1010bfb857d0.v2
license: CC BY 4.0
date-released: 2024-07-29