Skip to main content

Improved Quantum Information Set Decoding

  • Conference paper
  • First Online:
Post-Quantum Cryptography (PQCrypto 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10786))

Included in the following conference series:

Abstract

In this paper we present quantum information set decoding (ISD) algorithms for binary linear codes. First, we refine the analysis of the quantum walk based algorithms proposed by Kachigar and Tillich (PQCrypto’17). This refinement allows us to improve the running time of quantum decoding in the leading order term: for an n-dimensional binary linear code the complexity of May-Meurer-Thomae ISD algorithm (Asiacrypt’11) drops down from \(2^{0.05904n + o(n)}\) to \(2^{0.05806n+o(n)}\). Similar improvement is achieved for our quantum version of Becker-Jeux-May-Meurer (Eurocrypt’12) decoding algorithm. Second, we translate May-Ozerov Near Neighbour technique (Eurocrypt’15) to an ‘update-and-query’ language more common in a similarity search literature. This re-interpretation allows us to combine Near Neighbour search with the quantum walk framework and use both techniques to improve a quantum version of Dumer’s ISD algorithm: the running time goes down from \(2^{0.059962n+o(n)}\) to \(2^{0.059450+o(n)}\).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We omit sub-exponential in n factors throughout, because we are only interested in the constant \(\mathsf {c}\). Furthermore, our analysis is for an average case and we sometimes omit the word ‘expected’.

  2. 2.

    The (dimensionless) distances we consider here, denoted further \(\gamma , \alpha , \beta \), are all \(\le 1/2\), since we can flip the bits of the query point and search for ‘close’ rather than ‘far apart’ vectors.

  3. 3.

    By ‘error’ we mean missing a vector which is \(\gamma \)-close to \({\mathbf {q}}\).

  4. 4.

    One could also run Grover inside each bucket during the Query phase, when the buckets are larger than 1. This, however, does not seem to bring an improvement.

References

  1. Ambainis, A.: Quantum walk algorithm for element distinctness. In: FOCS, pp. 210–239 (2004)

    Google Scholar 

  2. Brouwer, A.E., Cohen, A.M., Neumaier, A.: Distance-Regular Graphs. Springer, Heidelberg (1989). https://doi.org/10.1007/978-3-642-74341-2

    Book  MATH  Google Scholar 

  3. Becker, A., Ducas, L., Gama, N., Laarhoven, T.: New directions in nearest neighbor searching with applications to lattice sieving. In: SODA 2016, pp. 10–24 (2016)

    Google Scholar 

  4. Bernstein, D.J.: Grover vs. McEliece. In: Sendrier, N. (ed.) PQCrypto 2010. LNCS, vol. 6061, pp. 73–80. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12929-2_6

    Chapter  Google Scholar 

  5. Bernstein, D.J., Jeffery, S., Lange, T., Meurer, A.: Quantum algorithms for the subset-sum problem. In: Gaborit, P. (ed.) PQCrypto 2013. LNCS, vol. 7932, pp. 16–33. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38616-9_2

    Chapter  Google Scholar 

  6. Becker, A., Joux, A., May, A., Meurer, A.: Decoding random binary linear codes in 2n/20: how 1 + 1 = 0 improves information set decoding. In: Pointcheval, D., Johansson, T. (eds.) EUROCRYPT 2012. LNCS, vol. 7237, pp. 520–536. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29011-4_31

    Chapter  Google Scholar 

  7. Both, L., May, A.: Optimizing BJMM with nearest neighbors: full decoding in \({2^{2 n/21}}\) and McEliece security. In: The Tenth International Workshop on Coding and Cryptography (2017)

    Google Scholar 

  8. Childs, A.M., Eisenberg, J.M.: Quantum algorithms for subset finding. Quantum Inf. Comput. 5(7), 593–604 (2005)

    MATH  Google Scholar 

  9. Christiani, T.: A framework for similarity search with space-time tradeoffs using locality-sensitive filtering. In: SODA, pp. 31–46 (2017)

    Google Scholar 

  10. Dumer, I.: On minimum distance decoding of linear codes. In: Proceedings of the 5th Joint Soviet-Swedish International Workshop on Information Theory, pp. 50–52 (1991)

    Google Scholar 

  11. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)

    Google Scholar 

  12. Kirshanova, E.: Improved quantum information set decoding (2018). http://perso.ens-lyon.fr/elena.kirshanova/Papers/quantumISD.pdf

  13. Kachigar, G., Tillich, J.-P.: Quantum information set decoding algorithms. In: Lange, T., Takagi, T. (eds.) PQCrypto 2017. LNCS, vol. 10346, pp. 69–89. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59879-6_5

    Chapter  Google Scholar 

  14. Laarhoven, T.: Tradeoffs for nearest neighbors on the sphere. CoRR, abs/1511.07527 (2015)

    Google Scholar 

  15. McEliece, R.J.: A public-key cryptosystem based on algebraic coding theory. In: Deep Space Network Progress Report, pp. 114–116 (1978)

    Google Scholar 

  16. May, A., Meurer, A., Thomae, E.: Decoding random linear codes in \(\tilde{\cal{O}}(2^{0.054n})\). In: Lee, D.H., Wang, X. (eds.) ASIACRYPT 2011. LNCS, vol. 7073, pp. 107–124. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25385-0_6

    Chapter  Google Scholar 

  17. Magniez, F., Nayak, A., Roland, J., Santha, M.: Search via quantum walk. SIAM J. Comput. 40(1), 142–164 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  18. May, A., Ozerov, I.: On computing nearest neighbors with applications to decoding of binary linear codes. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9056, pp. 203–228. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46800-5_9

    Google Scholar 

  19. Prange, E.: The use of information sets in decoding cyclic codes. IRE Trans. Inf. Theory 6, 5–9 (1962)

    Article  MathSciNet  Google Scholar 

  20. Schroeppel, R., Shamir, A.: A \({T}={O}(2^{n/2})\), \({S}={O}(2^{n/4})\) algorithm for certain NP-complete problems. SIAM J. Comput. 10, 456–464 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  21. Stern, J.: A method for finding codewords of small weight. In: Cohen, G., Wolfmann, J. (eds.) Coding Theory 1988. LNCS, vol. 388, pp. 106–113. Springer, Heidelberg (1989). https://doi.org/10.1007/BFb0019850

    Chapter  Google Scholar 

Download references

Acknowledgements

The author thanks Alexander May for enlightening discussions and suggestions. This work is supported by ERC Starting Grant ERC-2013-StG-335086-LATTAC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elena Kirshanova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kirshanova, E. (2018). Improved Quantum Information Set Decoding. In: Lange, T., Steinwandt, R. (eds) Post-Quantum Cryptography. PQCrypto 2018. Lecture Notes in Computer Science(), vol 10786. Springer, Cham. https://doi.org/10.1007/978-3-319-79063-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-79063-3_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-79062-6

  • Online ISBN: 978-3-319-79063-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics