Skip to main content
Log in

Quantum circuits for computing Hamming distance requiring fewer T gates

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Not applicable.

References

  1. Ladd TD, Jelezko F, Laflamme R, Nakamura Y, Monroe C, O’Brien JL (2010) Quantum computers. Nature 464(7285):45–53

    Article  Google Scholar 

  2. Shor PW (1999) Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev 41(2):303–332

    Article  MathSciNet  Google Scholar 

  3. Grover LK (1996) A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp 212–219

  4. Farhi E, Goldstone J, Gutmann S, Sipser M (2000) Quantum computation by adiabatic evolution. arXiv preprint quant-ph/0001106

  5. Raussendorf R, Briegel HJ (2001) A one-way quantum computer. Phys Rev Lett 86(22):5188

    Article  Google Scholar 

  6. Nielsen MA, Chuang IL (2010) Quantum computation and quantum information. Cambridge University Press, Cambridge, UK

    Google Scholar 

  7. Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79

    Article  Google Scholar 

  8. Torlai G, Melko RG (2020) Machine-learning quantum states in the NISQ era. Ann Rev Condens Matter Phys 11:325–344

    Article  Google Scholar 

  9. Roffe J (2019) Quantum error correction: an introductory guide. Contemp Phys 60(3):226–245

    Article  Google Scholar 

  10. Gottesman D (1998) Theory of fault-tolerant quantum computation. Phys Rev A 57(1):127

    Article  MathSciNet  Google Scholar 

  11. Pérez-Salinas A, Cervera-Lierta A, Gil-Fuster E, Latorre JI (2020) Data re-uploading for a universal quantum classifier. Quantum 4:226

    Article  Google Scholar 

  12. Orts F, Ortega G, Filatovas E, Garzón EM (2022) Implementation of three efficient 4-digit fault-tolerant quantum carry lookahead adders. J Supercomput 78(11):13323–41

    Article  Google Scholar 

  13. Orts F, Ortega G, Cucura A, Filatovas E, Garzón EM (2021) Optimal fault-tolerant quantum comparators for image binarization. J Supercomput 77(8):8433–8444

    Article  Google Scholar 

  14. Wiebe N, Kapoor A, Svore KM (2015) Quantum nearest-neighbor algorithms for machine learning. Quantum Inf Comput 15(3–4):318–358

    Google Scholar 

  15. Hamming RW (1986) Coding and information theory. Prentice-Hall Inc, New Jersey, US

    Google Scholar 

  16. Norouzi M, Fleet DJ, Salakhutdinov RR (2012) Hamming distance metric learning. Adv Neural Inform Process Syst, 25

  17. Zhang L, Zhang Y, Tang J, Lu K, Tian Q (2013) Binary code ranking with weighted Hamming distance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1586–1593

  18. Taheri R, Ghahramani M, Javidan R, Shojafar M, Pooranian Z, Conti M (2020) Similarity-based android malware detection using Hamming distance of static binary features. Futur Gener Comput Syst 105:230–247

    Article  Google Scholar 

  19. Raveendran N, Rengaswamy N, Rozpedek F, Raina A, Jiang L, Vasić B (2022) Finite rate QLDPC-GKP coding scheme that surpasses the CSS Hamming bound. Quantum 6:767

    Article  Google Scholar 

  20. Kathuria K, Ratan A, McConnell M, Bekiranov S (2020) Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne. Quantum Mach Intell 2(1):7

    Article  Google Scholar 

  21. Li J, Lin S, Yu K, Guo G (2022) Quantum K-nearest neighbor classification algorithm based on Hamming distance. Quantum Inf Process 21(1):18

    Article  MathSciNet  Google Scholar 

  22. Chomboon K, Chujai P, Teerarassamee P, Kerdprasop K, Kerdprasop N (2015) An empirical study of distance metrics for K-nearest neighbor algorithm. In: Proceedings of the 3rd International Conference on Industrial Application Engineering, Vol 2

  23. Barenco A, Bennett CH, Cleve R, DiVincenzo DP, Margolus N, Shor P, Sleator T, Smolin JA, Weinfurter H (1995) Elementary gates for quantum computation. Phys Rev A 52(5):3457

    Article  Google Scholar 

  24. Bernhardt C (2019) Quantum computing for everyone. Mit Press, Massachusetts, USA

    Book  Google Scholar 

  25. Miller DM, Soeken M, Drechsler R (2014) Mapping NCV circuits to optimized Clifford+T circuits. In: International Conference on Reversible Computation, pp 163–175. Springer

  26. Amy M, Maslov D, Mosca M, Roetteler M (2013) A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits. IEEE Trans Comput Aided Des Integr Circuits Syst 32(6):818–830

    Article  Google Scholar 

  27. Amy M, Maslov D, Mosca M (2014) Polynomial-time T-depth optimization of Clifford+ T circuits via matroid partitioning. IEEE Trans Comput Aided Des Integr Circuits Syst 33(10):1476–1489

    Article  Google Scholar 

  28. Litinski D (2019) Magic state distillation: not as costly as you think. Quantum 3:205

    Article  Google Scholar 

  29. Mohammadi M, Eshghi M (2009) On figures of merit in reversible and quantum logic designs. Quantum Inf Process 8:297–318

    Article  MathSciNet  Google Scholar 

  30. Thapliyal H, Muñoz-Coreas E (2019) Design of quantum computing circuits. IT Professional 21(6):22–26

    Article  Google Scholar 

  31. Thapliyal H, Muñoz-Coreas E, Khalus V (2021) Quantum circuit designs of carry lookahead adder optimized for T-count, T-depth and qubits. Sustain Comput Inform Syst 29:100457

    Google Scholar 

  32. Patterson DA, Hennessy JL (2016) Computer organization and design arm edition: the hardware software interface. Morgan kaufmann, Massachusetts, USA

    Google Scholar 

  33. Maslov D, Dueck GW (2003) Improved quantum cost for N-bit Toffoli gates. Electron Lett 39(25):1790–1791

    Article  Google Scholar 

  34. Muñoz-Coreas E, Thapliyal H (2018) Quantum circuit design of a T-count optimized integer multiplier. IEEE Trans Comput 68(5):729–739

    Article  MathSciNet  Google Scholar 

  35. Gosset D, Kliuchnikov V, Mosca M, Russo V (2013) An algorithm for the T-count. arXiv preprint arXiv:1308.4134

  36. Li H-S, Fan P, Xia H, Long G-L (2022) The circuit design and optimization of quantum multiplier and divider. Sci China Phys Mech Astron 65(6):260311

    Article  Google Scholar 

  37. Satoh T, Oomura S, Sugawara M, Yamamoto N (2022) Pulse-engineered controlled-v gate and its applications on superconducting quantum device. IEEE Trans Quantum Eng 3:1–10

    Article  Google Scholar 

  38. Hung WN, Song X, Yang G, Yang J, Perkowski M (2006) Optimal synthesis of multiple output boolean functions using a set of quantum gates by symbolic reachability analysis. IEEE Trans Comput Aided Des Integr Circuits Syst 25(9):1652–1663

    Article  Google Scholar 

  39. Gidney C (2018) Halving the cost of quantum addition. Quantum 2:74

    Article  Google Scholar 

  40. Orts F, Ortega G, Garzón EM (2022) Studying the cost of N-qubit Toffoli gates. In: International Conference on Computational Science, pp 122–128. Springer

  41. Thapliyal H (2016) Mapping of subtractor and adder-subtractor circuits on reversible quantum gates. In: Transactions on Computational Science XXVII, pp 10–34. Springer, New York, USA

  42. Qiskit contributors (2023) Qiskit: An open-source framework for quantum computing. 10.5281/zenodo.2573505

  43. Carrascal G, Del Barrio AA, Botella G (2021) First experiences of teaching quantum computing. J Supercomput 77:2770–2799

    Article  Google Scholar 

Download references

Funding

This work has been supported by the projects: S-MIP-21-53 (funded by the Research Council of Lithuania), PID2021-123278OB-I00 and PID2021-127836NB-I00 (funded by MCIN/AEI/10.13039/501100011033/ FEDER “A way to make Europe”); PID2020-119082RB-C22 (funded by MCIN/AEI/10.13039/501100011033); AYUD/2021/50994 and FC-GRUPIN-IDI/2018/000193 (funded by Gobierno del Principado de Asturias); Quantum Spain project funded by the Ministry of Economic Affairs and Digital Transformation of the Spanish Government and the European Union through the Recovery, Transformation and Resilience Plan - NextGenerationEU; and PID2021-123461NB-C22 (funded by MICIIN).

Author information

Authors and Affiliations

Authors

Contributions

FO involved in writing—original draft, provided software, and involved in execution of experiments; GO involved in writing—review and editing, done validation, and involved in visualization; EFC involved in orchestration, provided idea source, and involved in writing—original draft; IFR provided idea source, involved in formal analysis, and involved in writing—original draft; EMG involved in writing—review and editing, done validation, and involved in visualization.

Corresponding author

Correspondence to Francisco Orts.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Orts, F., Ortega, G., Combarro, E.F. et al. Quantum circuits for computing Hamming distance requiring fewer T gates. J Supercomput 80, 12527–12542 (2024). https://doi.org/10.1007/s11227-024-05916-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-05916-1

Keywords