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Approach for identifying the optimal set of qubits of quantum computing devices based on a model for generating binary random sequences

https://doi.org/10.32362/2500-316X-2025-13-6-25-46

EDN: XEUFSE

Abstract

Objectives. The absence of error-resistant quantum computers, coupled with the challenges associated with providing unrestricted and fully operational physical access to cloud quantum computing systems, prompts a critical examination of the necessity to develop universal and independent methods for evaluating and verifying cloud quantum computers. A promising approach involves evaluating the capabilities of a quantum computer in relation to its effectiveness in addressing specific challenges encountered in the assessment of information security systems. A potential test for ascertaining the performance and computational quality of a quantum computing device (QCD) is based on a model designed to generate a random binary sequence. By analyzing this sequence, insights can be obtained into the accuracy and reliability of the quantum register under study. The paper presents a software program developed for simulating the operation of a quantum random number generator.

Methods. The software implementation for interacting with cloud quantum computers was performed using the Qiskit open-source software kit. The graphical user interface of the software package was developed using a Qt5 crossplatform set of tools and widgets for creating applications. The analysis of the generated binary sequence was performed using a set of statistical tests NIST STS2.

Results. The developed software package provides users with a graphical interface for conducting an analysis of a cloud QCD to identify the optimal and most error-resistant set of qubits. The findings from experiments conducted on three cloud quantum computing devices are reported.

Conclusions. The proposed approach, which is constrained by limitations of computing power and duration of access to cloud-based QCD, imposes minimal demands on the productive capabilities of the quantum system. It offers clear and unequivocally interpretable insights into the technical characteristics of a cloud quantum computer, while also being reproducible, easily scalable, and universally applicable. 

About the Authors

A. V. Korolkov
MIREA – Russian Technological University
Russian Federation

Andrey V. Korolkov, Cand. Sci. (Eng.), Corresponding Member of the Academy of Cryptography of the Russian Federation, Corresponding Member of the A.M. Prokhorov Academy of Engineering Sciences of the Russian Federation, Head of the Department of Information Security, Institute of Artificial Intelligence, MIREA – Russian Technological University  

78, Vernadskogo pr., Moscow, 119454 


Competing Interests:

The authors declare no conflicts of interest.



A. A. Kryuchkov
MIREA – Russian Technological University
Russian Federation

Andrey A. Kryuchkov, Senior Lecturer, Department of Information Security, Institute of Artificial Intelligence 

78, Vernadskogo pr., Moscow, 119454 


Competing Interests:

The authors declare no conflicts of interest.



References

1. Proctor T., Young K., Baczewski A.D., Blume-Kohout R. Benchmarking quantum computers. arXiv. 2024. arXiv:2407.08828. https://doi.org/10.48550/arXiv.2407.08828

2. Amico M., Zhang H., Jurcevic P., et al. Defining Standard Strategies for Quantum Benchmarks. IBM Publications. 2023. Available from URL: https://research.ibm.com/publications/defining-standard-strategies-for-quantum-benchmarks. Accessed May 15, 2025.

3. Acuaviva A., Aguirre D., Pena R., Sanz M. Benchmarking Quantum Computers: Towards a Standard Performance Evaluation Approach. arXiv. 2024. arXiv:2407.10941. https://doi.org/10.48550/arXiv.2407.10941

4. Eisert J., Hangleiter D., Walk N., et al. Quantum certification and benchmarking. Nat. Rev. Phys. 2020;2:382–390. https://doi.org/10.1038/s42254-020-0186-4

5. Kryuchkov A.A. On the need to adopt a single standard for evaluating the performance and certification of quantum computers. Informatsionno-ehkonomicheskie aspekty standartizatsii i tekhnicheskogo regulirovaniya = Information and Economic Aspects of Standardization and Technical Regulation. 2024;6(81):43–49 (in Russ.).

6. Wack A., Paik H., Javadi-Abhari A., Jurcevic P., Faro I., Gambetta J.M., Johnson B.R. Scale, Quality, and Speed: three key attributes to measure the performance of near-term quantum computers. arXiv. 2021. arXiv:2110.14108. https://doi.org/10.48550/arXiv.2110.14108

7. McKay D.C., Hincks I., Pritchett E.J., Carroll M., Govia L.C.G., Merkel S.T. Benchmarking Quantum Processor Performance at Scale. arXiv. 2023. arXiv:2311.05933. https://doi.org/10.48550/arXiv.2311.05933

8. Amico M., Zhang H., Jurcevic P., Bishop L.S., Nation P., Wack A., McKay D.C. Defining Standard Strategies for Quantum Benchmarks. arXiv. 2023. arXiv:2303.02108 https://doi.org/10.48550/arXiv.2303.02108

9. Shor P.W. Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings 35th Annual Symposium on Foundations of Computer Science. IEEE; 1994. P. 124–134. https://doi.org/10.1109/SFCS.1994.365700

10. Balygin K.A., Kulik S.P., Molotkov S.N. Implementation of a Quantum Generator of Random Numbers: Extraction of Provably Random Bit Sequences from Correlated Markov Chains. Jetp. Lett. 2024;119(7):538–548. https://doi.org/10.1134/S0021364024600575 [Original Russian Text: Balygin K.A., Kulik S.P., Molotkov S.N. Implementation of a Quantum Generator of Random Numbers: Extraction of Provably Random Bit Sequences from Correlated Markov Chains. Pis’ma v Zhurnal ehksperimental’noi i teoreticheskoi fiziki (Pis’ma v ZHEHTF). 2024;119(7):533–544 (in Russ.). https://doi.org/10.31857/S1234567824070115 ]

11. Gaidash A.A., Goncharov R.K., Kozubov A.V., Yakovlev P.V. Mathematical model of random number generator based on vacuum fluctuations. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informatika. Protsessy upravleniya = Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes. 2024;20(2):136–153 (in Russ.). https://doi.org/10.21638/spbu10.2024.202

12. Petrenko A.A., Kovalev A.V., Bougrov V.E. Random number generation with arrays of coupled quantum-dot micropillar lasers. Nauchno-tekhnicheskii vestnik informatsionnykh tekhnologii, mekhaniki i optiki = Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2021;21(6):962–968 (in Russ.). https://doi.org/10.17586/2226-1494-2021-21-6-962-968

13. Orlov M.A., Nechaev K.A., Reznichenko S.A. Evaluation of statistical properties and cryptographic strength of random sequences obtained by an IBM quantum computer. Bezopasnost’ informatsionnykh tekhnologii = IT Security (Russia). 2023;30(1):14–26 (in Russ.). http://doi.org/10.26583/bit.2023.1.01

14. Li Y., Fei Y., Wang W., et al. Quantum random number generator using a cloud superconducting quantum computer based on source-independent protocol. Sci Rep. 2021;11:23873. https://doi.org/10.1038/s41598-021-03286-9

15. Salehi R., Razaghi M., Fotouhi B. Hybrid Hadamard and Controlled-Hadamard Based Quantum Random Number Generators in IBM QX. Physica Scripta. 2022;97(6):065101. https://doi.org/10.1088/1402-4896/ac698b

16. Yadav A., Mishra S., Pathak A. Partial loopholes free device-independent quantum random number generator using IBM’s quantum computers. Physica Scripta. 2024;99(11):115103. https://doi.org/10.1088/1402-4896/ad7c02

17. Feynman R.P. Quantum Mechanical Computers. Optics News. 1985;11(2):11–20. Available from URL: https://www.opticaopn.org/home/articles/on/volume_11/issue_2/features/quantum_mechanical_computers/. Accessed May 15, 2025.

18. Kryuchkov A.A. QISs_v.0.3.9: Computer Program RU2025613655 RF. Publ. 13.02.2025 (in Russ.).

19. Kryuchkov A.A., Komogorov K.E. Simulation of the random number generation process on quantum computing devices. In: Proceedings of the 8th Scientific and Practical Conference “Actual Problems and Prospects of Radio Engineering and Infocommunication Systems.” Moscow: RTU MIREA; 2024. P. 501–506 (in Russ.).

20. Acharya R., Abanin D.A., Aghababaie-Beni L., et al. Quantum error correction below the surface code threshold. Nature. 2025;638:920–926. https://doi.org/10.1038/s41586-024-08449-y

21. Verma S., Kumari S.S., Kumar R.S. Topological quantum error correction with semions. Int. J. Phys. Math. 2024:6(2):44–47. https://doi.org/10.33545/26648636.2024.v6.i2a.95

22. Webster M., Browne D. Engineering Quantum Error Correction Codes Using Evolutionary Algorithms. IEEE Trans. Quantum Eng. 2025;6:3100514. https://doi.org/10.1109/TQE.2025.3538934


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For citations:


Korolkov A.V., Kryuchkov A.A. Approach for identifying the optimal set of qubits of quantum computing devices based on a model for generating binary random sequences. Russian Technological Journal. 2025;13(6):25-46. https://doi.org/10.32362/2500-316X-2025-13-6-25-46. EDN: XEUFSE

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ISSN 2782-3210 (Print)
ISSN 2500-316X (Online)