Quantum Software Security

Authors

Anshu Kumari Pandey

Department of Computer Science, Institute of Technology and Management, Gorakhpur (India)

Mohd Nadeem

Department of Computer Science and Engineering, Shri Ramswaroop Memorial University, Barabanki (India)

Article Information

DOI: 10.47772/IJRISS.2026.100300451

Subject Category: Computer Science

Volume/Issue: 10/3 | Page No: 6225-6240

Publication Timeline

Submitted: 2026-03-28

Accepted: 2026-04-02

Published: 2026-04-12

Abstract

The rapid advancement of quantum computing poses an unprecedented and existential threat to the cryptographic foundations underpinning modern software systems. Classical asymmetric cryptographic primitives — including RSA, Elliptic Curve Cryptography (ECC), and the Diffie-Hellman key exchange — are provably vulnerable to Shor's algorithm, executable on sufficiently powerful quantum hardware. This data analysis study presents a comprehensive investigation of quantum computing security threats across the software design lifecycle, employing a Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to systematically quantify and prioritize eight critical security dimensions: confidentiality, integrity, availability, authentication, non-repudiation, key management, side-channel resistance, and forward secrecy. Data were collected through structured expert surveys involving 47 domain specialists, supplemented by empirical performance benchmarks of six post-quantum cryptographic (PQC) algorithms standardized or under consideration by the National Institute of Standards and Technology (NIST) in 2024. Our Fuzzy-AHP analysis yields a global Consistency Ratio (CR) of 0.047, well within the acceptable threshold of 0.10, validating the reliability of expert judgments. Results demonstrate that confidentiality (weight: 0.920) and integrity (weight: 0.880) are the highest-priority security dimensions in the quantum threat context. Comparative data analysis of classical, hybrid, and full post-quantum deployments reveals that quantum attack resistance improves by 9,300% under full PQC adoption relative to classical cryptography alone, while introducing a 663% increase in key exchange latency. A five-phase quantum-safe software design framework is proposed and validated against the SDLC. The study concludes with actionable guidance for software architects, security engineers, and organizational decision-makers navigating the transition to quantum-resilient software infrastructure.

Keywords

Quantum Computing Security, Software Design, Fuzzy-AHP, Post-Quantum Cryptography

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References

1. Shad Kirmani and Padma Raghavan. 2013. Scalable parallel graph partitioning. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC '13). Association for Computing Machinery, New York, NY, USA, Article 51, 1–10. https://doi.org/10.1145/2503210.2503280 [Google Scholar] [Crossref]

2. Kirmani S, Park J, Raghavan P. An embedded sectioning scheme for multiprocessor topology-aware mapping of irregular applications. The International Journal of High Performance Computing Applications. 2017;31(1):91-103. doi:10.1177/1094342015597082 [Google Scholar] [Crossref]

3. S. Kirmani and M. Shankar, “Generating keywords by associative context with input words,” US Patent US10699302B2, Jun. 30, 2020. [Online]. Available: https://patents.google.com/patent/US10699302B2/en [Google Scholar] [Crossref]

4. S. Kirmani and K. Madduri, "Spectral Graph Drawing: Building Blocks and Performance Analysis," 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Vancouver, BC, Canada, 2018, pp. 269-277, doi: 10.1109/IPDPSW.2018.00053 [Google Scholar] [Crossref]

5. S. Kirmani, H. Sun and P. Raghavan, "A Scalability and Sensitivity Study of Parallel Geometric Algorithms for Graph Partitioning," 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Lyon, France, 2018, pp. 420-427, doi: 10.1109/CAHPC.2018.8645916. [Google Scholar] [Crossref]

6. Ashirbad Mishra, Shad Kirmani, and Kamesh Madduri. 2020. Fast Spectral Graph Layout on Multicore Platforms. In Proceedings of the 49th International Conference on Parallel Processing (ICPP '20). Association for Computing Machinery, New York, NY, USA, Article 45, 1–11. https://doi.org/10.1145/3404397.3404471 [Google Scholar] [Crossref]

7. Tyler J, Pastor J, Huhns MN, Kirmani S, Du H. Exposing, formalizing and reasoning over the latent semantics of tags in multimodal data sources. Applied Ontology. 2013;8(2):95-130. doi:10.3233/AO-130124 [Google Scholar] [Crossref]

8. Mishra, S. Kirmani and K. Madduri, "Fast Sentence Classification using Word Co-occurrence Graphs*," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 620-629, doi: 10.1109/BigData62323.2024.10825869. [Google Scholar] [Crossref]

9. S. Kirmani, “Exploiting Graph Embedding for Parallelism and Performance,” Ph.D. dissertation, Dept. of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA, 2014. Available: https://etda.libraries.psu.edu/catalog/27325 [Google Scholar] [Crossref]

10. F. Kirmani, B. J. Lane and J. R. Rose, "Exploring Machine Learning Techniques to Improve Peptide Identification," 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 2019, pp. 66-71, doi: 10.1109/BIBE.2019.00021. [Google Scholar] [Crossref]

11. Fawad Kirmani, Bryan Lane, and John Rose. 2025. Identifying Proteotypic Peptides via Deep Learning. In Proceedings of the 11th International Conference on Bioinformatics Research and Applications (ICBRA '24). Association for Computing Machinery, New York, NY, USA, 42–47. https://doi.org/10.1145/3700666.3700691 [Google Scholar] [Crossref]

12. Fawad Kirmani, Ananthavishnu S Unni, Varsha P Kulkarni, Kyle Lackey, John R Rose, Detecting polar ring galaxies via deep learning, RAS Techniques and Instruments, Volume 4, 2025, rzaf043, https://doi.org/10.1093/rasti/rzaf043 [Google Scholar] [Crossref]

13. Kirmani, F., “Detecting Strongly-Lensed Supernovae in Wide-field Space Telescope Imaging via Deep Learning”, arXiv e-prints, Art. no. arXiv:2512.19886, 2025. doi:10.48550/arXiv.2512.19886. [Google Scholar] [Crossref]

14. Alharbi et al., “Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective,” BMC Med. Inform. Decis. Mak., vol. 24, no. 1, p. 240, 2024, doi: 10.1186/s12911-024-02651-8. [Google Scholar] [Crossref]

15. M. Nadeem, “Analyze quantum security in software design using fuzzy-AHP,” Int. J. Inf. Technol., 2024, doi: 10.1007/s41870-024-02002-w. [Google Scholar] [Crossref]

16. Alharbi et al., “A Link Analysis Algorithm for Identification of Key Hidden Services,” Comput. Mater. Contin., vol. 68, no. 1, 2021, doi: 10.32604/cmc.2021.016887. [Google Scholar] [Crossref]

17. Attaallah, S. Khatri, M. Nadeem, S. A. Ansar, A. K. Pandey, and A. Agrawal, “Prediction of COVID-19 pandemic spread in Kingdom of Saudi Arabia,” Comput. Syst. Sci. Eng., vol. 37, no. 3, 2021, doi: 10.32604/CSSE.2021.014933. [Google Scholar] [Crossref]

18. S. A. Khan, M. Nadeem, A. Agrawal, R. A. Khan, and R. Kumar, “Quantitative analysis of software security through fuzzy promethee-ii methodology: A design perspective,” Int. J. Mod. Educ. Comput. Sci., vol. 13, no. 6, 2021, doi: 10.5815/ijmecs.2021.06.04. [Google Scholar] [Crossref]

19. M. Nadeem et al., “Multi-level hesitant fuzzy based model for usable-security assessment,” Intell. Autom. Soft Comput., vol. 31, no. 1, 2022, doi: 10.32604/IASC.2022.019624. [Google Scholar] [Crossref]

20. M. Alenezi, M. Nadeem, A. Agrawal, R. Kumar, and R. A. Khan, “Fuzzy multi criteria decision analysis method for assessing security design tactics for web applications,” Int. J. Intell. Eng. Syst., vol. 13, no. 5, 2020, doi: 10.22266/ijies2020.1031.17. [Google Scholar] [Crossref]

21. M. Ahmad et al., “Healthcare device security assessment through computational methodology,” Comput. Syst. Sci. Eng., vol. 41, no. 2, 2022, doi: 10.32604/csse.2022.020097. [Google Scholar] [Crossref]

22. H. Alyami et al., “The evaluation of software security through quantum computing techniques: A durability perspective,” Appl. Sci., vol. 11, no. 24, 2021, doi: 10.3390/app112411784. [Google Scholar] [Crossref]

23. W. Alosaimi et al., “Analyzing the impact of quantum computing on IoT security using computational based data analytics techniques,” AIMS Math., vol. 9, no. 3, pp. 7017–7039, 2024, doi: 10.3934/math.2024342. [Google Scholar] [Crossref]

24. Alharbi et al., “Managing Software Security Risks through an Integrated Computational Method,” Intell. Autom. Soft Comput., vol. 28, no. 1, p. 179, Mar. 2021, doi: 10.32604/IASC.2021.016646. [Google Scholar] [Crossref]

25. S. H. Almotiri, M. Nadeem, M. A. Al Ghamdi, and R. A. Khan, “Analytic Review of Healthcare Software by Using Quantum Computing Security Techniques,” Int. J. Fuzzy Log. Intell. Syst., vol. 23, no. 3, pp. 336–352, Sep. 2023, doi: 10.5391/IJFIS.2023.23.3.336. [Google Scholar] [Crossref]

26. M. Nadeem, M. Ahmad, M. Ahmad, P. C. Pathak, S. Gupta, and H. Pandey, “Evaluating the Factors of CGTMSE Scheme in Bank by Using Fuzzy AHP,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), 2023, vol. 6, pp. 56–61, doi: 10.1109/IC3I59117.2023.10397669. [Google Scholar] [Crossref]

27. F. A. Alzahrani, M. Ahmad, M. Nadeem, R. Kumar, and R. A. Khan, “Integrity Assessment of Medical Devices for Improving Hospital Services,” Comput. Mater. Contin., vol. 67, no. 3, p. 3619, Mar. 2021, doi: 10.32604/CMC.2021.014869. [Google Scholar] [Crossref]

28. P. C. Pathak, M. Nadeem, and S. A. Ansar, “Security assessment of operating system by using decision making algorithms,” Int. J. Inf. Technol., 2024, doi: 10.1007/s41870-023-01706-9. [Google Scholar] [Crossref]

29. Masood Ahmad, F. Al-Amri, “Healthcare Device Security Assessment through Computational Methodology,” Comput. Syst. Sci. Eng., vol. 41, no. 2, pp. 811–828, 2022, doi: 10.32604/csse.2022.020097. [Google Scholar] [Crossref]

30. H. Alyami et al., “Analyzing the data of software security life-span: Quantum computing era,” Intell. Autom. Soft Comput., vol. 31, no. 2, 2022, doi: 10.32604/iasc.2022.020780. [Google Scholar] [Crossref]

31. F. A. Alzahrani, M. Ahmad, M. Nadeem, R. Kumar, and R. A. Khan, “Integrity Assessment of Medical Devices for Improving Hospital Services,” Comput. Mater. Contin., vol. 67, no. 3, 2021, doi: 10.32604/cmc.2021.014869. [Google Scholar] [Crossref]

32. F. Alassery, A. Alzahrani, A. I. Khan, A. Khan, M. Nadeem, and M. T. J. Ansari, “Quantitative Evaluation of Mental-Health in Type-2 Diabetes Patients Through Computational Model,” Intell. Autom. Soft Comput., vol. 32, no. 3, 2022, doi: 10.32604/IASC.2022.023314. [Google Scholar] [Crossref]

33. M. Nadeem, “Deep Learning Approach for Classifying DDoS Attack Traffic in SDN Environments”, JISCR, vol. 7, no. 2, pp. 109-126, Dec. 2024. [Google Scholar] [Crossref]

34. Mohd Nadeem, Amal Krishna Sarkar, Mohammed Ishrat, "Securing information systems through quantum computing Grover's algorithm approach", Computational Intelligence Applications in Cyber Security, 1st Edition, 2024. [Google Scholar] [Crossref]

35. Mohd Nadeem, Prabhash Chandra Pathak, Masood Ahmad, Nafees Akhter Farooqui, "Identification of security factors in cloud computing Defence security perspective", Computational Intelligence Applications in Cyber Security, 1st Edition, 2024. [Google Scholar] [Crossref]

36. J. Preskill, "Quantum computing in the NISQ era and beyond," Quantum, vol. 2, p. 79, 2018, doi: 10.22331/q-2018-08-06-79. [Google Scholar] [Crossref]

37. P. W. Shor, "Algorithms for quantum computation: Discrete logarithms and factoring," in Proc. 35th Annu. Symp. Found. Comput. Sci., 1994, pp. 124–134. [Google Scholar] [Crossref]

38. L. K. Grover, "A fast quantum mechanical algorithm for database search," in Proc. 28th Annu. ACM Symp. Theory Comput., 1996, pp. 212–219. [Google Scholar] [Crossref]

39. NIST, "Post-Quantum Cryptography Standards," FIPS 203/204/205, National Institute of Standards and Technology, 2024. [Online]. Available: https://www.nist.gov/pqcrypto [Google Scholar] [Crossref]

40. M. Cerezo et al., "Variational quantum algorithms," Nat. Rev. Phys., vol. 3, no. 9, pp. 625–644, 2021. [Google Scholar] [Crossref]

41. F. Alassery, A. Alzahrani, A. I. Khan, A. Khan, M. Nadeem, and M. T. J. Ansari, "Quantitative evaluation of mental-health in type-2 diabetes patients through computational model," Intell. Autom. Soft Comput., vol. 32, no. 3, 2022, doi: 10.32604/IASC.2022.023314. [Google Scholar] [Crossref]

42. T. L. Saaty, The Analytic Hierarchy Process. New York, NY: McGraw-Hill, 1980. [Google Scholar] [Crossref]

43. D.-Y. Chang, "Applications of the extent analysis method on fuzzy AHP," Eur. J. Oper. Res., vol. 95, no. 3, pp. 649–655, 1996. [Google Scholar] [Crossref]

44. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th Anniversary ed. Cambridge, UK: Cambridge Univ. Press, 2010. [Google Scholar] [Crossref]

45. E. Moguel et al., "A roadmap for quantum software engineering: Applying the lessons learned from the classics," IEEE Softw., vol. 39, no. 1, pp. 28–35, 2022. [Google Scholar] [Crossref]

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