STUDY OF POSSIBILITIES OF USING PHOTONIC AND QUANTUM COMPUTING TECHNOLOGIES TO CALCULATE EXACT PROBABILITY DISTRIBUTIONS OF STATISTIC VALUES FROM FINITE DISCRETE SEQUENCES
Abstract
This article explores the feasibility of using photonic and quantum computing technologies to calculate exact probability distributions of discrete sequence statistics, assuming the existence of working hardware prototypes of computing systems and the development of the required quantum algorithms. The performance evaluation of computing systems based on photonic computing technologies is based on materials from the Sarov Scientific Center for Physics and Microphysics of the Russian Academy of Sciences. The performance of a quantum computing system is assessed by comparing the time it takes to solve a boson sampling problem from a given distribution on a computing system with known performance and the time it takes to solve it on a quantum computing system. To assess the feasibility of using photonic and quantum computing technologies to calculate exact distributions, modern methods for calculating them are considered. These methods are based on solving the type multiplicity equation and a system of linear equations in non-negative integers. Analytical expressions determining the computational complexity of these methods are presented. The values of the boundaries of the parameters of exact distributions accessible for calculation using photonic and quantum computing technologies are determined. A comparison of the obtained results with the results of using multiprocessor computing technologies to calculate exact distributions using various methods is presented. An analysis of the feasibility of using photonic and quantum computing technologies to calculate exact distributions is conducted by comparing the number of parameter pairs that can be calculated for exact distributions with the total number of distribution parameters within the Fisher region, which determines a fivefold increase in sample size over the alphabet size. An analysis of the data on the number of sample parameters shows that with increasing performance of the computing technologies used, the ability to calculate exact distributions increases. However, even with the most powerful quantum technologies, this number does not exceed one-tenth of the total number of exact distributions required for statistical analysis of discrete sequences in alphabets up to 256 characters long
References
1. Mel'nikov S.Yu., Meshcheryakov R.V., Peresypkin V.A. Nekotorye aspekty primeneniya tekhnologiy iskusstvennogo intellekta v zadachakh zashchity informatsii (Obzor) [Some aspects of application of arti-ficial intelligence technologies in information security (review)], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2024, No. 5 (241), pp. 58-68. ISSN 1999-9429. DOI 10.18522/2311-3103-2024-5-58-68.
2. Mel'nikov A.K., Ronzhin A.F. Obobshchennyy statisticheskiy metod analiza tekstov, osnovannyy na raschete raspredeleniy veroyatnosti znacheniy statistik [Generalized statistical method of text analysis based on calculation of probability distribution of statistical values], Informatika i ee primeneniya [In-formatics and its applications], 2016, Vol. 10, Issue 4 (October-December), pp. 89-95. ISSN 1992-2264.
3. Mel'nikov A.K. Slozhnost' rascheta tochnykh raspredeleniy veroyatnosti simmetrichnykh additivno razdelyaemykh statistik i oblast' primeneniya predel'nykh raspredeleniy [The com-plexity of calculating the exact probability distributions of symmetric additive-separated sta-tistics and the application of limit distributions], Doklady TUSUR [Proceedings of Tomsk State University of Control Systems and Radio-electronics]. Tomsk, 2017, Vol. 20, No. 4, pp. 126-130. ISSN 1818-0442. doi: 10.21293/1818-0442-2017-20-4-126-130.
4. Zelyukin N.B., Mel'nikov A.K. Slozhnost' rascheta tochnykh raspredeleniy veroyatnosti znacheniy statis-tik i oblast' primeneniya predel'nykh raspredeleniy [The complexity of calculating exact probability dis-tributions of statistical values and the scope of application of limit distributions], Elektronnye sredstva i sistemy upravleniya: Mater. dokladov XIII Mezhdunar. nauch.-prakt. konf. (29 noyabrya –
1 dekabrya 2017 g.) [Electronic means and control systems: Proceedings of the XIII International scien-tific and practical conference (November 29 – December 1, 2017)]: In 2 part. Part 2. Tomsk:
V-Spektr, 2017, pp. 84-90. ISNB 978-591191-364-9.
5. Spisok TOP500 redaktsiya ot 06/2025 [TOP500 list, edition 06/2025]. Available at: https://www.top500.org (accessed 08 July 2025).
6. Sozdan prototip komp'yutera, rabotayushchego so «skorost'yu sveta» [A prototype computer that works at the “speed of light” has been created]. Available at: https://news.mail.ru/society/65613497/ (accessed 08 April 2025).
7. Rossiyskie uchenye k 2027 godu predstavyat komp'yuter, rabotayushchiy «so skorost'yu sveta» [Rus-sian scientists to present computer operating at “speed of light” by 2027]. Available at: https://habr.com/ru/news/863000/ (accessed 08 April 2025).
8. Aliev F.K., Bukin E.G., Korol'kov A.V., Matveev E.A. Kvantovaya fotonnaya komp'yuternaya tekhnologi-ya resheniya slozhnykh vychislitel'nykh zadach sistem vysokoy dostupnosti [Quantum photonic com-puter technology for solving complex computational problems of high-availability systems], Sistemy vysokoy dostupnosti [High availability systems], 2021, Vol. 17, No. 14, pp. 34-54. DOI: https://doi.org/10.18127/j20729472-202104-03.
9. Yaponskiy superkomp'yuter Fugaku uprochil svoe liderstvo v spiske TOP500 [Japanese supercomputer Fugaku strengthened its leadership in the TOP500 list]. Available at: https.//www.ixbt.com/. news/2020/ 11/18/japonskij-superkompjuter-fugaku-uprochil-svoe-liderstvo-v-spiske-top500.html (accessed 25 March 2021).
10. Han-Sen Zhong, Hui Wang, Yu-Hao Deng et al. Quantum computational advantage using photons. Available at: https://science. sciencemag.org/.content/370/6523/1460.full (accessed 15 October 2024).
11. Han-Sen Zhong et al. Phase Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light. Phys. Rev. Lett. 127/180502 Published 25 October 2021.
12. Lars S. Madsen et al. Quantum computational advantage with a programmable fotonic processor, Na-ture, 2022, Vol. 606, pp. 75-81. DOI: 10. 1038/s41586-022-04725-x.
13. Fisher R.A. Statisticheskie metody dlya issledovateley [Statistical methods for researchers]: transl. from Engl. Moscow: Gosstatizdat., 1958, 73 p.
14. Mel'nikov A.K. Primenenie tochnykh i predel'nykh priblizheniy raspredeleniy veroyatnostey znacheniy statistik pri reshenii zadachi obrabotke tekstov [Application of exact and limit approximations of statistics probability distributions for the problem of text processing], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2018, No. 8 (202), pp. 114-135. ISSN 1999-9429. doi: 10.23683/2311-3103-2018-8-114-135.
15. Zubkov A.M., Filina M.V. Tochnoe vychislenie raspredeleniy s pomoshch'yu tsepey Markova [Exact calculation of distributions using Markov chains], PDM. Prilozhenie [PDM. Appendix], 2012, No. 5, pp. 114-116.
16. Filina M.V. Algoritmy tochnogo vychisleniya raspredeleniy statistiki Pirsona i rezul'taty chislennykh eksperimentov [Algorithms for the exact calculation of Pearson statistics distributions and the results of numerical experiments], XV Vserossiyskaya konferentsiya molodykh uchenykh po matematicheskomu modelirovaniyu i informatsionnym tekhnologiyam (Tyumen', 29–31 oktyabrya 2014 g.), IVT SO RAN, Novosibirsk, 2014 [XV All-Russian Conference of Young Scientists on Mathematical Modeling and Information Technology (Tyumen, October 29–31, 2014), ICT SB RAS, Novosibirsk, 2014], pp. 52.
17. Zubkov A.M., Filina M.V. Algoritm tochnogo vychisleniya raspredeleniy razdelimykh statistik i ego primeneniya [An algorithm for the exact calculation of distributions of separable statistics and its applications], Analiticheskie i vychislitel'nye metody v teorii veroyatnostey i ee prilozheniyakh (Moskva, 23–27 oktyabrya 2017 g.) [Analytical and computational methods in probability theory and its applications (Moscow, October 23–27, 2017)], red. A.V. Lebedev, RUDN, Moscow, 2017, pp. 490-494.
18. Zubkov A.M., Filina M.V. Vychislenie raspredeleniy statistik s pomoshch'yu tsepey Markova [Calculating distributions of statistics using Markov chains], Diskretnaya matematika [Discrete Mathe-matics], 2020, 32:4, pp. 38-51.
19. Mel'nikov A.K. Algoritmicheskaya slozhnost' rascheta tochnykh priblizheniy raspredeleniy veroyatnostey znacheniy statistik metodom resheniya uravneniya pervoy kratnosti tipov [Algorithmic complexity of calculating exact approximations of probability distributions of statistical values by solving the equation of the first multiplicity of types], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2020, No. 7 (217), pp. 52-67. ISSN 1999-9429. DOI 10.18522/2311-3103-2020-7-52-67.
20. Mel'nikov A.K., Levin I.I., Dordopulo A.I., Slasten L.M. Otsenka vozmozhnostey perspektivnykh vychislitel'nykh tekhnologiy dlya rascheta tochnykh priblizheniy raspredeleniy veroyatnostey znacheniy statistik [Analysis of advanced computer technologies for calculation of exact approximations of statis-tics probability distributions], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sci-ences], 2022, No. 4 (228), pp. 50-62. ISSN 1999-9429. DOI 10.18522/2311-3103-2022-4-50-62.
21. Mel'nikov A.K. Ogranichenie kolichestva razlichnykh oprobuemykh vektorov dlya polucheniya vsekh resheniy sistemy lineynykh uravneniy vtoroy kratnosti na mnogoprotsessornoy vychislitel'noy sisteme [Limiting the number of different test vectors to obtain all solutions of a system of linear equations of the second multiplicity on multiprocessor computer system], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2021, No. 2 (219), pp. 167-181. ISSN 1999-9429. doi: 10.18522/2311-3103-2021-2-167-181.
22. Kramer G. Matematicheskie metody statistiki [Mathematical methods of statistics]: transl. from Engl. Moscow: Mir, 1975, 648 p.
23. Mel'nikov A.K. Vybor metoda rascheta tochnykh priblizheniy diskretnykh raspredeleniy veroyatnostey znacheniy statistik [Choosing the Method of Exact Approximations of Discrete Statistics Probability Distributions], Vestnik komp'yuternykh i informatsionnykh tekhnologiy [Vestnik komp'yuternyh i infor-matsionnyh tekhnologiy], 2021, Vol. 18, No. 6 (204), pp. 39-48. DOI 10.14489/vkit.2021.06.pp.039-048.








