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

DOI:10.4121/21294714.v1
The DOI displayed 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

Usage statistics

544
views
246
downloads

Categories

Time coverage

2020

Licence

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)