Skip to main content

Advertisement

Log in

Quantum cache memory: a framework for enhancing DNA analysis through quantum computing

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

This research explores the application of quantum computing to DNA analysis, focusing on transitioning classical data to quantum information formats. We developed the Quantum Cache Memory (QCM) framework, which utilizes superposition and hybrid encoding via entanglement. The QCM framework is designed to preserve the integrity of genetic sequences throughout the quantum computing process. The effectiveness of this approach is demonstrated through implementations of single nucleotide polymorphism (SNP) detection and pattern search algorithms using a perfect quantum simulator. The results demonstrate the potential for leveraging quantum phenomena to process classical data in parallel on quantum hardware. However, the limitations of current quantum hardware and data encoding efficiency are acknowledged. This study shows the groundwork for future improvements in quantum computing ecosystems, such as the need for persistent quantum states and more effective handling of large-scale data. Our research has been conducted solely through simulations and mathematical modeling, indicating the necessity for future work on actual quantum servers.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

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

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 1484–1509 (1997). https://doi.org/10.1137/S0097539795293172

    Article  MathSciNet  Google Scholar 

  2. Grover LK (1996) A fast quantum mechanical algorithm for database search. In: Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC ’96. ACM Press, Philadelphia, pp 212–219

  3. Blekos, K., Brand, D., Ceschini, A., et al.: A review on quantum approximate optimization algorithm and its variants. Phys. Rep. 1068, 1–66 (2024). https://doi.org/10.1016/j.physrep.2024.03.002

    Article  ADS  MathSciNet  Google Scholar 

  4. Tilly, J., Chen, H., Cao, S., et al.: The variational quantum eigensolver: a review of methods and best practices. Phys. Rep. 986, 1–128 (2022). https://doi.org/10.1016/j.physrep.2022.08.003

    Article  ADS  MathSciNet  Google Scholar 

  5. Jäger, J., Krems, R.V.: Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines. Nat. Commun. 14, 576 (2023). https://doi.org/10.1038/s41467-023-36144-5

    Article  ADS  Google Scholar 

  6. Clark, D.P., Pazdernik, N.J.: DNA, RNA, and protein. In: Biotechnology, pp. 33–61. Elsevier, New York (2016)

    Chapter  Google Scholar 

  7. Pearson, W.R.: Rapid and sensitive sequence comparison with FASTP and FASTA. In: Methods in Enzymology, pp. 63–98. Elsevier, New York (1990)

    Google Scholar 

  8. Shastry, B.S.: SNPs in disease gene mapping, medicinal drug development and evolution. J. Hum. Genet. 52, 871–880 (2007). https://doi.org/10.1007/s10038-007-0200-z

    Article  Google Scholar 

  9. Innan, N., Khan, M.A.-Z.: Classical-to-quantum sequence encoding in genomics. https://doi.org/10.48550/ARXIV.2304.10786 (2023)

  10. Kösoglu-Kind, B., Loredo, R., Grossi, M., et al.: A biological sequence comparison algorithm using quantum computers. Sci. Rep. 13, 14552 (2023). https://doi.org/10.1038/s41598-023-41086-5

    Article  ADS  Google Scholar 

  11. Chang, Y.-F.: Extensive quantum theory of DNA and biological string. Neuroquantology 12, 738 (2014). https://doi.org/10.14704/nq.2014.12.3.738

    Article  Google Scholar 

  12. Bhabhatsatam, B., Smanchat, S.: Hybrid quantum encoding: combining amplitude and basis encoding for enhanced data storage and processing in quantum computing. In: 2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 512–516. IEEE, Phitsanulok (2023)

    Chapter  Google Scholar 

  13. Weigold, M., Barzen, J., Leymann, F., Salm, M.: Encoding patterns for quantum algorithms. IET Quantum Commun. 2, 141–152 (2021). https://doi.org/10.1049/qtc2.12032

    Article  Google Scholar 

  14. Giovannetti, V., Lloyd, S., Maccone, L.: Quantum random access memory. Phys. Rev. Lett. 100, 160501 (2008). https://doi.org/10.1103/PhysRevLett.100.160501

    Article  ADS  MathSciNet  Google Scholar 

  15. Shepherd, D., Bremner, M.J.: Temporally unstructured quantum computation. Proc. R. Soc. A 465, 1413–1439 (2009). https://doi.org/10.1098/rspa.2008.0443

    Article  ADS  MathSciNet  Google Scholar 

  16. Khan, M.A., Aman, M.N., Sikdar, B.: Beyond bits: a review of quantum embedding techniques for efficient information processing. IEEE Access 12, 46118–46137 (2024). https://doi.org/10.1109/ACCESS.2024.3382150

    Article  Google Scholar 

  17. Phalak, K., Chatterjee, A., Ghosh, S.: Quantum random access memory for dummies. Sensors 23, 7462 (2023). https://doi.org/10.3390/s23177462

    Article  ADS  Google Scholar 

  18. Park, D.K., Petruccione, F., Rhee, J.-K.K.: Circuit-based quantum random access memory for classical data. Sci. Rep. 9, 3949 (2019). https://doi.org/10.1038/s41598-019-40439-3

    Article  ADS  Google Scholar 

  19. Djordjevic, I.: Quantum algorithms. In: Quantum information processing and quantum error correction, pp. 145–173. Elsevier, New York (2012)

    Chapter  Google Scholar 

  20. Sang, J., Yu, C.: Understanding quantum parallelism through programming. In: 2023 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1072–1078. IEEE, Las Vegas (2023)

    Chapter  Google Scholar 

  21. Niroula, P., Nam, Y.: A quantum algorithm for string matching. npj Quantum Inf. 7, 37 (2021). https://doi.org/10.1038/s41534-021-00369-3

    Article  ADS  Google Scholar 

  22. Soni, K.K., Rasool, A.: Quantum-effective exact multiple patterns matching algorithms for biological sequences. PeerJ Comput. Sci. 8, e957 (2022). https://doi.org/10.7717/peerj-cs.957

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

B.B. conceptualized the study, developed the architecture, wrote the code, set up the environment, conducted testing, analyzed and validated the results, and wrote and edited the main manuscript text. S.S. supervised the research, analyzed and validated the results, and contributed to writing and editing the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Bhattaraprot Bhabhatsatam.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Appendix

Appendix

See Figs.

Fig. 10
figure 10

Processing Pipeline for 8-Nucleotide Data and Motif in FASTA Format through QCM

10,

Fig. 11
figure 11

SNPs Processing Pipeline for 8-Nucleotide Data and Motif through QCM and Quantum Comparator Circuit

11 and

Fig. 12
figure 12

Quantum Adaptation of the Sliding Window Algorithm for Pattern Matching

12.

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

Bhabhatsatam, B., Smanchat, S. Quantum cache memory: a framework for enhancing DNA analysis through quantum computing. Quantum Inf Process 23, 390 (2024). https://doi.org/10.1007/s11128-024-04595-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-024-04595-4

Keywords

Navigation