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Journal of Russian Laser Research

, Volume 39, Issue 6, pp 558–567 | Cite as

Error Estimation at the Information Reconciliation Stage of Quantum Key Distribution

  • E. O. Kiktenko
  • A. O. Malyshev
  • A. A. Bozhedarov
  • N. O. Pozhar
  • M. N. Anufriev
  • A. K. Fedorov
Article
  • 9 Downloads

Abstract

Quantum key distribution (QKD) offers a practical solution for secure communication between two distinct parties via a quantum channel and an authentic public channel. In this work, we consider different approaches to the quantum bit error rate (QBER) estimation at the information reconciliation stage of the post-processing procedure. For reconciliation schemes employing low-density parity-check (LDPC) codes, we develop a novel syndrome-based QBER estimation algorithm. The algorithm suggested is suitable for irregular LDPC codes and takes into account punctured and shortened bits. Testing our approach in a real QKD setup, we show that an approach combining the proposed algorithm with conventional QBER estimation techniques allows one to improve the accuracy of the QBER estimation.

Keywords

quantum key distribution information reconciliation error estimation LDPC codes 

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References

  1. 1.
    P. W. Shor, SIAM J. Comput., 26, 1484 (1997).MathSciNetCrossRefGoogle Scholar
  2. 2.
    N. Gisin, G. Ribordy, W. Tittel, and H. Zbinden, Rev. Mod. Phys., 74, 145 (2002).ADSCrossRefGoogle Scholar
  3. 3.
    H.-K. Lo, M. Curty, and K. Tamaki, Nat. Photon., 8, 595 (2014).ADSCrossRefGoogle Scholar
  4. 4.
    E. Diamanti, H.-K. Lo, and Z. Yuan, J. Quantum Inform., 2, 16025 (2016).CrossRefGoogle Scholar
  5. 5.
    C. H . Bennet and G. Brassard, in: Proceedings of the IEEE International Conference on Computers, Systems, and Signal Processing (Bangalore, India, 1984), IEEE Publ., New York (1984), p. 175.Google Scholar
  6. 6.
    R. Gallager, IRE Trans. Inform. Theor., 8, 21 (1962).MathSciNetCrossRefGoogle Scholar
  7. 7.
    D. J. C. MacKay, IEEE Trans. Inform. Theor., 45, 399 (1999).CrossRefGoogle Scholar
  8. 8.
    A. Shokrollahi, “Coding, cryptography, and combinatorics,” in: Keqin Feng, Harald Niederreiter, and Chaoping Xing (Eds.), Progress in Computer Science and Applied Logic, Springer (2004), Vol. 23, p. 85.Google Scholar
  9. 9.
    D. Elkouss, J. Martínez-Mateo, and V. Martin, in: Proceedings of the IEEE International Symposium on Information Theory and its Applications (ISITA), (Taichung, Taiwan, 2010), IEEE Publ., New York (2010), p. 179.Google Scholar
  10. 10.
    D. A. Kronberg, Mat. Vopr. Kriptogr., 8(2), 77 (2017).MathSciNetCrossRefGoogle Scholar
  11. 11.
    D. Elkouss, J. Martínez-Mateo, and V. Martin, Quantum Inform. Comput., 11, 226 (2011).Google Scholar
  12. 12.
    J. Martínez-Mateo, D. Elkouss, and V. Martin, Quantum Inform. Comput., 12, 791 (2012).Google Scholar
  13. 13.
    E. O. Kiktenko, A. S. Trushechkin, Y. V. Kurochkin, and A. K. Fedorov, J. Phys. Conf. Ser., 741, 012081 (2016).CrossRefGoogle Scholar
  14. 14.
    E. O. Kiktenko, A. S. Trushechkin, C. C. W. Lim, et al., Phys. Rev. Appl., 8, 044017 (2017).ADSCrossRefGoogle Scholar
  15. 15.
    A. K. Fedorov, E. O. Kiktenko, and A. S. Trushechkin, Lobachevskii J. Math., 39, 992 (2018).MathSciNetCrossRefGoogle Scholar
  16. 16.
    P. Treeviriyanupab, in: 14th International Symposium on Communications and Information Technologies (ISCIT), (Incheon, 2014), p. 351.Google Scholar
  17. 17.
    X.-Y. Hu, E. Eleftheriou, D.-M. Arnold, and A. Dholakia, in: Proceedings of Global Telecommunications Conference (2001), p. 1879.Google Scholar
  18. 18.
    M. P. C. Fossorier, M. Mihaljevic, and H. Imai, IEEE Trans. Commun., 47, 683 (1999).CrossRefGoogle Scholar
  19. 19.
    A. A. Emran and M. Elsabrouty, in: The 11th IEEE Consumer Communications and Networking Conference (CCNC), (Las Vegas, NV, 2014), p. 518.Google Scholar
  20. 20.
    D. Slepian and J. Wolf, IEEE Trans. Inform. Theory, 19, 471 (1973).CrossRefGoogle Scholar
  21. 21.
    R. P. Brent, Algorithms for Minimization without Derivatives, Prentice-Hall, Englewood Cliffs, New Jersey (1973).zbMATHGoogle Scholar
  22. 22.
    J. Martínez-Mateo, D. Elkouss, and V. Martin, IEEE Commun. Lett., 14, 1155 (2010).CrossRefGoogle Scholar
  23. 23.
    D. Elkouss, A. Leverrier, R. Alleaume, and J. J. Boutros in: Proceedings of IEEE International Symposium on Information Theory (2009), p. 1879.Google Scholar
  24. 24.
    A. V. Duplinskiy, E. O. Kiktenko, N. O. Pozhar, et al., J. Russ. Laser Res., 39, 113 (2018).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • E. O. Kiktenko
    • 1
    • 2
    • 3
  • A. O. Malyshev
    • 1
    • 4
    • 5
  • A. A. Bozhedarov
    • 1
    • 6
  • N. O. Pozhar
    • 1
  • M. N. Anufriev
    • 1
  • A. K. Fedorov
    • 1
  1. 1.Russian Quantum CenterMoscowRussia
  2. 2.Steklov Mathematical Institute of the Russian Academy of SciencesMoscowRussia
  3. 3.Geoelectromagnetic Research Centre of Schmidt Institute of Physics of the Earth of the Russian Academy of SciencesMoscowRussia
  4. 4.Moscow Institute of Physics and TechnologyDolgoprudnyRussia
  5. 5.Institute for Information Transmission Problems of the Russian Academy of SciencesMoscowRussia
  6. 6.Skolkovo Institute of Science and TechnologyMoscowRussia

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