Prielinger, Luise; Gómez Iñesta, Álvaro; Vardoyan, Gayane (2024): Data underlying the publication: Surrogate-guided Optimization in Quantum Networks. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/a07a9e97-f34c-4e7f-9f68-1010bfb857d0.v1

Dataset

x choose version: version 3 - 2024-09-13 (latest) version 2 - 2024-07-29

• Artificial Intelligence and Image Processing
• Other Information and Computing Sciences
• Distributed Computing
• Quantum Physics
• Physical Sciences
• Information and Computing Sciences

bayesian optimization, entanglement distribution, machine learning, quantum networks, surrogate optimization

2023-2024

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Luise Prielinger , Álvaro Gómez Iñesta , Gayane Vardoyan

This data is associated with the paper "Surrogate-guided Optimization in Quantum Networks".

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.

• 2024-07-17 first online, published, posted

4TU.ResearchData

image/jpg, image/pdf, tables/csv, readme/md

• NWO funding 2020–2024 Part I ‘Fundamental Research’ (grant code 601.QT.001-1) Dutch Research Council (NWO)
• NWO QSC grant (grant code BGR2 17.269.) Dutch Research Council (NWO)

QuTech, Delft University of Technology