Skip to main content
Log in

Simulation algorithm on the quantum BB84 protocol based on Monte Carlo method in classical computer environment

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

A simulation algorithm based on the law of large number and Monte Carlo method in the classical computer environment is presented. During the simulation of the original quantum BB84 protocol in ideal environment, the sender Alice tries to send classical bit 0 or 1 to the receiver Bob, and the eavesdropper Eve tries to get the transmission information by intercepting and resending the quantum particles. The bit error rate in the quantum BB84 protocol is also given, and the value of the bit error rate can be analyzed if Eve eavesdrops the communication. In addition, the mean square error is introduced to describe the similarity between the simulation data and the theoretical data. (The smaller the mean square error is, the more reasonable the simulation will be.) In this simulation, the value of MSE is \(6.705\times 10^{-5}\) after 5000 times simulation when Eve eavesdrops the communication with the probability of 100%. The time complexity of the simulation algorithm is O(n) in our experiment. The reason why there is always an error between the simulation data and theoretical data is analyzed, and the correctness and rationality of the simulation algorithm are also analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Shannon, C.E.: Communication theory of secrecy systems. Bell Syst. Tech. J. 28(4), 656 (1949)

    Article  MathSciNet  MATH  Google Scholar 

  2. Vernam, G.S.: Cipher printing telegraph systems. J. A.I.E.E. 45(6), 572 (1926). https://doi.org/10.1109/JAIEE.1926.6537250

    Article  Google Scholar 

  3. Jian, L., Na, L., Zhang, Y., Wen, S., Wei, D., Chen, W., Wenping, M.: A survey on quantum cryptography. Chin. J. Electron. 27(2), 223 (2018)

    Article  Google Scholar 

  4. Na, W., Fu, J., Bhargava, B.K., Zeng, J.: Efficient retrieval over documents encrypted by attributes in cloud computing. IEEE Trans. Inf. Forensics Secur. 13(10), 2653 (2018)

    Article  Google Scholar 

  5. Wang, N., Fu, J., Li, J., Bhargava, B.K.: Source-location privacy protection based on anonymity cloud in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 15(1), 100 (2020)

    Article  Google Scholar 

  6. Wang, N., Fu, J., Zeng, J., Bhargava, B.K.: Source-location privacy full protection in wireless sensor networks. Inf. Sci. 444, 105 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cormen, T.T., Leiserson, C.E., Rivest, R.L.: Introduction to algorithms. Resonance 1(9), 14 (2003)

    Google Scholar 

  8. Howard, R.: Data encryption standard. Comput. Secur. 6(3), 195 (1977)

    Google Scholar 

  9. US Department of Commerce, NIST: Advanced encryption standard. In: National Computer Conference, pp. 83–87 (1997)

  10. Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings 35th Annual Symposium on Foundations of Computer Science, pp. 124–134 (1994)

  11. Allati, A.E., Baz, M.E., Hassouni, Y.: Quantum key distribution via tripartite coherent states. Quantum Inf. Process. 10(5), 589 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhou, X.Y., Zhang, C.H., Zhang, C.M., Wang, Q.: Obtaining better performance in the measurement-device-independent quantum key distribution with heralded single-photon sources. Phys. Rev. A 96(5), 052337 (2017)

    Article  ADS  Google Scholar 

  13. Lo, H.K., Chau, H.F.: Unconditional security of quantum key distribution over arbitrarily long distances., Science 283(5410), 2050 (1999)

    Article  ADS  Google Scholar 

  14. Frederic, G., Philippe, G.: Continuous variable quantum cryptography using coherent states. Phys. Rev. Lett. 88(5), 057902 (2002)

    Article  Google Scholar 

  15. Shapiro, J.H., Zhuang, Q., Zhang, Z., Dove, J., Wong, F.N.C.: Floodlight quantum key distribution. In: Lasers Congress 2016 (ASSL, LSC, LAC), pp. 1–2. Optical Society of America (2016). https://doi.org/10.1364/LSC.2016.LTu5B.1

  16. Lo, H.K., Curty, M., Qi, B.: Measurement-device-independent quantum key distribution. Phys. Rev. Lett. 108, 130503 (2012). https://doi.org/10.1103/PhysRevLett.108.130503

    Article  ADS  Google Scholar 

  17. Yang, Y.G., Teng, Y.W., Chai, H.P., Wen, Q.Y.: Revisiting the security of secure direct communication based on ping-pong protocol [Quantum Inf. Process. 8, 347 (2009)]. Quantum Inf. Process. 10(3), 317 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hwang, T., Luo, Y.P., Yang, C.W., Lin, T.H.: Quantum authencryption: one-step authenticated quantum secure direct communications for off-line communicants. Quantum Inf. Process. 13(4), 925 (2014)

    Article  ADS  Google Scholar 

  19. Yang, C.W., Hwang, T.: Improved QSDC protocol over a collective-dephasing noise channel. Int. J. Theor. Phys. 51(12), 3941 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  20. Chang, H.H., Heo, J.O., Khym, G.L., Lim, J., Hong, S.K., Yang, H.J.: Quantum channels are sufficient for multi-user quantum key distribution protocol between users. Opt. Commun. 283(12), 2644 (2010)

    Article  ADS  Google Scholar 

  21. Wang, T.Y., Wen, Q.Y., Chen, X.B.: Cryptanalysis and improvement of a multi-user quantum key distribution protocol. Opt. Commun. 283(24), 5261 (2010)

    Article  ADS  Google Scholar 

  22. Cai, X.Q., Wang, T.Y., Wei, C.Y., Gao, F.: Cryptanalysis of multiparty quantum digital signatures. Quantum Inf. Process. 18(8), 252 (2019)

    Article  ADS  MathSciNet  Google Scholar 

  23. Bennett, C.H., Brassard, G.: Quantum cryptography: public key distribution and coin tossing. In: Proceedings of the IEEE International Conference on Computers, Systems, and Signal Processing, pp. 175–179. IEEE, New York (1984)

  24. Sibson, P., Erven, C., Godfrey, M., Miki, S., Yamashita, T., Fujiwara, M., Sasaki, M., Terai, H., Tanner, M.G., Natarajan, C.M.: Chip-based quantum key distribution. Nat. Commun. 8, 13984 (2017)

    Article  ADS  Google Scholar 

  25. Yin, H.L., Chen, T.Y., Yu, Z.W., Liu, H., You, L.X., Zhou, Y.H., Chen, S.J., Mao, Y., Huang, M.Q., Zhang, W.J.: Measurement-device-independent quantum key distribution over a 404 km optical fiber. Phys. Rev. Lett. 117(19), 190501 (2016)

    Article  ADS  Google Scholar 

  26. Liao, S.K., Cai, W.Q., Liu, W.Y., Zhang, L., Li, Y., Ren, J.G., Yin, J., Shen, Q., Cao, Y., Li, Z.P.: Satellite-to-ground quantum key distribution. Nature 549(7670), 43 (2017)

    Article  ADS  Google Scholar 

  27. Li, J., Li, N., Li, L.L., Wang, T.: One step quantum key distribution based on EPR entanglement. Sci. Rep. 6, 28767 (2016)

    Article  ADS  Google Scholar 

  28. Jian, L., Yang, Y.G., Chen, X.B., Zhou, Y.H., Shi, W.M.: Practical quantum private database queries based on passive round-robin differential phase-shift quantum key distribution. Sci. Rep. 6, 31738 (2016)

    Article  ADS  Google Scholar 

  29. Gottesman, D.: The Heisenberg Representation of Quantum Computers. arXiv e-prints arXiv:quant-ph/9807006 (1998)

  30. Aaronson, S., Gottesman, D.: Improved simulation of stabilizer circuits. Phys. Rev. A 70(5), 0406196 (2004)

    Article  Google Scholar 

  31. Boger, G.: Spreadsheet simulation of the law of large numbers. Math. Comput. Educ. 39(Fall), 175 (2005)

    Google Scholar 

  32. Revesz, P.: The laws of large numbers. Technometrics 11(3), 625 (1968)

    MATH  Google Scholar 

  33. Ohno, K., Esfarjani, K., Kawazoe, Y.: Monte Carlo Methods, pp. 195–270. Springer, Berlin (1999)

    Google Scholar 

  34. Cevallos-Robalino, L.E., Garcia-Fernandez, G.F., Gallego, E., Guzman-Garcia, K.A., Vega-Carrillo, H.R.: Study by Monte Carlo methods of an explosives detection system made up with a D-D neutron generator and NaI(Tl) gamma detectors. Appl. Radiat. Isot. 141, 167 (2018)

    Article  Google Scholar 

  35. Goda, T., Murakami, D., Tanaka, K., Sato, K.: Decision-theoretic sensitivity analysis for reservoir development under uncertainty using multilevel quasi-Monte Carlo methods. Comput. Geosci. 22(4), 1 (2018)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. U1636106, 61572053) and the China Postdoctoral Science Foundation (Grant No. 2019M650020). A demo will be available at corresponding author’s github.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leilei Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Li, L., Li, H. et al. Simulation algorithm on the quantum BB84 protocol based on Monte Carlo method in classical computer environment. Quantum Inf Process 19, 335 (2020). https://doi.org/10.1007/s11128-020-02836-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-020-02836-w

Keywords

Navigation