Replication data for: Optimizing Entanglement Generation and Distribution Using Genetic Algorithms

doi: 10.4121/21294714.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21294714
Datacite citation style:
Francisco Ferreira da Silva; Ariana Torres-Knoop; Tim Coopmans; David Maier; Wehner, S.D.C. (Stephanie) (2022): Replication data for: Optimizing Entanglement Generation and Distribution Using Genetic Algorithms. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21294714.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Delft University of Technology logo
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categories
time coverage
2020
licence
cc-0.png logo CC0

This is the data corresponding to the publication "Optimizing Entanglement Generation and Distribution Using Genetic Algorithms" (https://doi.org/10.1088/2058-9565/abfc93). It contains the raw data resulting from all the optimization runs performed (see publication for details), from parameter scans and scripts used to generate plots.


history
  • 2022-10-07 first online, published, posted
publisher
4TU.ResearchData
funding
  • Quantum Internet Alliance (grant code 820445) [more info...] European Commission
organizations
QuTech and Kavli Institute of Nanoscience, Delft University of Technology

DATA

files (1)