Free versus bound entanglement, a NP-hard problem tackled by machine learning

Beatrix C. Hiesmayr

doi:10.1038/s41598-021-98523-6

Free versus bound entanglement, a NP-hard problem tackled by machine learning

Beatrix C. Hiesmayr (Corresponding author)

Quantum Science

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	19739
Number of pages	13
Journal	Scientific Reports
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41598-021-98523-6
Publication status	Published - 5 Oct 2021

Funding

B.C. Hiesmayr acknowledges gratefully the Austrian Science Fund (FWF-P26783). The author thanks for the fruitful discussions with Wojtek Krzemie, Dietmar Millinger, Franz Ramskogler and Wolfgang Waltenberger on ML algorithms and many thanks to Dan McNuty for carefully reading the manuscript. Furthermore, the Vienna Scientific Cluster (VSC) is acknowledged and the great support of their team.

Austrian Fields of Science 2012

103025 Quantum mechanics

Keywords

SEPARABILITY
STATES

Access to Document

10.1038/s41598-021-98523-6Licence: CC BY 4.0

https://phaidra.univie.ac.at/o:1597571Licence: CC BY 4.0
https://arxiv.org/abs/2106.03977Licence: Other

Cite this

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Free versus bound entanglement, a NP-hard problem tackled by machine learning

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

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Ein quanteninformations-theoretischer Zugang zu Systemen in der Hochenergiephysik

Cite this

Free versus bound entanglement, a NP-hard problem tackled by machine learning

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Ein quanteninformations-theoretischer Zugang zu Systemen in der Hochenergiephysik

Cite this