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Implementation of a quantum random number generator based on the optimal clustering of photocounts

  • Quantum Informatics
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Abstract

To implement quantum random number generators, it is fundamentally important to have a mathematically provable and experimentally testable process of measurements of a system from which an initial random sequence is generated. This makes sure that randomness indeed has a quantum nature. A quantum random number generator has been implemented with the use of the detection of quasi-single-photon radiation by a silicon photomultiplier (SiPM) matrix, which makes it possible to reliably reach the Poisson statistics of photocounts. The choice and use of the optimal clustering of photocounts for the initial sequence of photodetection events and a method of extraction of a random sequence of 0’s and 1’s, which is polynomial in the length of the sequence, have made it possible to reach a yield rate of 64 Mbit/s of the output certainly random sequence.

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References

  1. D. N. Klyshko, Photons and Nonlinear Optics (Nauka, Moscow, 1980; Gordon and Breach, New York, 1988).

    Google Scholar 

  2. D. N. Klyshko and A. V. Masalov, Phys. Usp. 38, 1203 (1995).

    Article  ADS  Google Scholar 

  3. M. Herrero-Collantes and J. Carlos Garcia-Escartin, Rev. Mod. Phys. 89, 015004 (2017).

    Article  ADS  Google Scholar 

  4. Y. Okawachi, M. Yu, K. Luke, D. O. Carvalho, M. Lipson, and A. L. Gaeta, Opt. Lett. 41, 4194 (2016).

    Article  ADS  Google Scholar 

  5. F.-X. Wang, C. Wang, W. Chen, S. Wang, F.-S. Lv, D.-Y. He, Z.-Q. Yin, H.-W. Li, G.-C. Guo, and Z.-F. Han, J. Light Wave Technol. 33, 3319 (2015).

    Article  ADS  Google Scholar 

  6. Q. Yan, B. Zhao, Z. Hua, Q. Liao, and H. Yang, Rev. Sci. Instrum. 86, 073113 (2015).

    Article  ADS  Google Scholar 

  7. Y.-Q. Nie, H.-F. Zhang, Z. Zhang, J. Wang, X. Ma, J. Zhang, and J.-W. Pan, Appl. Phys. Lett. 104, 051110 (2014).

    Article  ADS  Google Scholar 

  8. K. S. Kravtsov, I. V. Radchenko, S. P. Kulik, and S. N. Molotkov, J. Opt. Soc. Am. 32, 1743 (2015); arXiv:1507.02059.

    Article  ADS  Google Scholar 

  9. I. V. Radchenko, Cand. Sci. (Phys. Math.) Dissertation (Prokhorov Inst. Gen. Phys. RAS, Moscow, 2015).

    Google Scholar 

  10. S. N. Molotkov, JETP Lett. 105, 395 (2017).

    Article  ADS  Google Scholar 

  11. C. Gabriel, C. Wittmann, D. Sych, R. Dong, W. Mauerer, U. L. Andersen, C. Marquardt, and G. Leuchs, Nat. Photon. Lett. 4, 771 (2010).

    Google Scholar 

  12. J. Yang, J. Liu, Q. Su, Z. Li, F. Fan, B. Xu, and H. Guo, Opt. Express 24, 27475 (2016).

    Article  ADS  Google Scholar 

  13. L. Trevisan, J. ACM 48, 860 (2001).

    Article  MathSciNet  Google Scholar 

  14. W. Mauerer, C. Portmann, and V. B. Scholz, arXiv:1212.0520 (2012).

  15. R. König, R. Renner, and C. Schaffner, IEEE Trans. Inform. Theory 55, 4337 (2009).

    Article  MathSciNet  Google Scholar 

  16. X. Ma, F. Xu, H. Xu, X. Tan, B. Qi, and H.-K. Lo, Phys. Rev. A 87, 062327 (2013).

    Article  ADS  Google Scholar 

  17. D. Frauchiger, R. Renner, and M. Troyer, arXiv:1311.4547 (2013).

  18. D. Stucki, S. Burri, E. Charbon, C. Chunnilall, A. Meneghetti, and F. Regazzoni, Proc. SPIE 8899, 88990R (2013).

    Article  ADS  Google Scholar 

  19. Q. Yan, B. Zhao, Q. Liao, and N. Zhou, Rev. Sci. Instrum. 85, 103116 (2014).

    Article  ADS  Google Scholar 

  20. V. F. Babkin, Probl. Peredachi Inform. 7, 13 (1971).

    MathSciNet  Google Scholar 

  21. D. E. Knuth, The Art of Computer Programming (Addison Wesley, Cambridge, 1981), Vol. 2.

    MATH  Google Scholar 

  22. G. Marsaglia, Die Hard: A Battery of Tests for Random Number Generators. http://stat.fsu.edu/pub/diehard.

  23. P. L’Ecuyer and R. Simard, TestU01: A Software Library in ANSI C for Empirical Testing of Random Number Generators. http://www.iro.umontreal. ca/lecuyer. Accessed 2002.

  24. A Statistical Test Suite for the Validation of Random Number Generators and Pseudo Random Number Generators for Cryptographic Applications. http://csrc.nist.gov/rng/SP800-22b.pdf.

  25. H. Cramer, Mathematical Methods of Statistics (Asia Publ., London, 1946).

    MATH  Google Scholar 

  26. D. A. Kalashnikov, Si-Hui Tan, and L. A. Krivitsky, Opt. Express 20, 5044 (2012).

    Article  ADS  Google Scholar 

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Authors and Affiliations

  1. Faculty of Physics, Moscow State University, Moscow, 119991, Russia

    K. A. Balygin, V. I. Zaitsev, A. N. Klimov & S. P. Kulik

  2. Prokhorov General Physics Institute, Russian Academy of Sciences, Moscow, 119991, Russia

    A. N. Klimov

  3. Institute of Solid State Physics, Russian Academy of Sciences, Chernogolovka, Moscow region, 142432, Russia

    S. N. Molotkov

  4. Academy of Cryptography of the Russian Federation, Moscow, 121552, Russia

    S. N. Molotkov

  5. Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, 119991, Russia

    S. N. Molotkov

Authors
  1. K. A. Balygin
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  2. V. I. Zaitsev
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  3. A. N. Klimov
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  4. S. P. Kulik
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  5. S. N. Molotkov
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Corresponding author

Correspondence to S. N. Molotkov.

Additional information

Original Russian Text © K.A. Balygin, V.I. Zaitsev, A.N. Klimov, S.P. Kulik, S.N. Molotkov, 2017, published in Pis’ma v Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki, 2017, Vol. 106, No. 7, pp. 451–458.

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Balygin, K.A., Zaitsev, V.I., Klimov, A.N. et al. Implementation of a quantum random number generator based on the optimal clustering of photocounts. Jetp Lett. 106, 470–476 (2017). https://doi.org/10.1134/S0021364017190043

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  • DOI: https://doi.org/10.1134/S0021364017190043

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