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  Deep-learning-based radio-frequency side-channel attack on quantum key distribution

Baliuka, A., Stöcker, M., Auer, M., Freiwang, P., Weinfurter, H., & Knips, L. (2023). Deep-learning-based radio-frequency side-channel attack on quantum key distribution. Physical Review Applied, 20: 054040. doi:10.1103/PhysRevApplied.20.054040.

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 Creators:
Baliuka, Adomas1, 2, Author
Stöcker, Markus1, 2, Author
Auer, Michael1, 2, Author
Freiwang, Peter1, 2, Author
Weinfurter, Harald2, 3, Author           
Knips, Lucas2, 3, Author           
Affiliations:
1external, ou_persistent22              
2MCQST - Munich Center for Quantum Science and Technology, External Organizations, ou_3330166              
3Laser Spectroscopy, Max Planck Institute of Quantum Optics, Max Planck Society, ou_1445568              

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Language(s): eng - English
 Dates: 2023-08-022023-10-182023-11-202023-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevApplied.20.054040
Other: 6484
 Degree: -

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Grant ID : -
Funding program : Germany’s Excellence Strategy EXC- 2111 390814868
Funding organization : DFG
Project name : European project OpenQKD
Grant ID : -
Funding program : -
Funding organization : European Commission (EC)
Project name : DE-QOR and QUBE-II
Grant ID : -
Funding program : -
Funding organization : BMBF

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Title: Physical Review Applied
  Abbreviation : Phys. Rev. Appl.
Source Genre: Journal
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Publ. Info: College Park, Md. [u.a.] : American Physical Society
Pages: - Volume / Issue: 20 Sequence Number: 054040 Start / End Page: - Identifier: ISSN: 2331-7019
CoNE: https://pure.mpg.de/cone/journals/resource/2331-7019