Factoring Decision Support System Based on Optimized Quantum Algorithms QMC

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Abstract

The continuous growth of financial markets dictates the need for its participants to seek new approaches to financial analysis to gain competitive advantages, including through the use of new approaches in the field of computing. Quantum computing can be used as a tool for obtaining these advantages over competitors. In particular, Monte Carlo modeling, although widely used in financial risk management, requires significant computing resources due to the large number of scenarios required to obtain an accurate result. To optimize this approach, quantum amplitude estimation algorithms are used, which accelerate this process if pre-calculated probability distributions are used to initialize input quantum states. However, in the absence of these distributions in existing approaches to this topic, they are generated numerically using classical computing, which completely negates the advantage of the quantum approach. This article proposes a solution to this problem by using quantum computing, including for the generation of probability distributions. The article discusses the creation of quantum circuits for modeling the evolution of risk factors over time for capital flows, interest rates, and credit risks, and presents the combination of these models with quantum amplitude estimation algorithms as an example of using the obtained algorithms for credit risk management. In conclusion, the article analyzes the possibility of using the obtained circuits in financial analysis.

About the authors

A. V Chuvakov

Samara State Technical University

Email: avch2105@gmail.com
Molodogvardeyskaya St. 244

R. O Boryaev

Samara State Technical University

Email: r.boryaev@gmail.com
Molodogvardeyskaya St. 244

References

  1. Orús R., Mugel S., Lizaso E. Quantum computing for finance: Overview and prospects // Reviews in Physics. 2019. vol. 4. doi: 10.1016/j.revip.2019.100028.
  2. Egger D.J., Gambella C., Marecek J., McFaddin S., Mevissen M., Raymond R., Simonetto A., Woerner S., Yndurain E. Quantum computing for finance: State-of-the-art and future prospects // IEEE Transactions on Quantum Engineering. 2020. vol. 1. pp. 1–24. doi: 10.1109/TQE.2020.3030314.
  3. Gómez A., Rodriguez A.L., Manzano A., Nogueiras M., Ordóñez G., Vázquez C. A Survey on quantum computational finance for derivatives pricing and VaR // Archives of Computational Methods in Engineering. 2022. vol. 29. pp. 4137–4163. doi: 10.1007/s11831-022-09732-9.
  4. Herman D., Googin C., Liu X., Galda A., Safro I., Sun Y., Pistoia M., Alexeev Y.A. Survey of Quantum Computing for Finance // 2022. arxiv preprint arxiv: 2201.02773.
  5. Wilkens S., Moorhouse J. Quantum computing for financial risk measurement // Quantum Information Processing. 2023. vol. 22. doi: 10.1007/s11128-022-03777-2.
  6. Intallura P., Korpas G., Chakraborty S., Kungurtsev V., Marecek J. A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond // 2023. arxiv preprint arxiv: 2303.04945.
  7. Dalzell A.M., McArdle S., Berta M., Bienias P., Chen C., Gilyén A., Hann C.T., Kastoryano M.J., Khabiboulline E.T., Kubica A., Salton G., Wang S., Brandão F.G.S.L. Quantum algorithms: A survey of applications and end-to-end complexities // 2023. arxiv preprint arxiv:2310.03011.
  8. Romano C., Di Climemnte A. Measuring Portfolio Value-at-Risk by a Copula-EVT Based Approach // Studi economici. 2005. vol. 2005(85). pp. 29–65.
  9. Brassard G., Høyer P., Mosca M., Tapp A. Quantum amplitude amplification and estimation // Quantum Computation and Information. 2002. vol. 305. pp. 53–74. doi: 10.1090/conm/305/05215.
  10. Herbert S. No quantum speedup with grover-rudolph state preparation for quantum monte carlo integration // Physical Review E. 2021. vol. 103(6-1). doi: 10.1103/PhysRevE.103.063302.
  11. Vazquez A.C., Woerner S. Efficient state preparation for quantum amplitude estimation // Physical Review Applied. 2021. vol. 15(3). doi: 10.1103/PhysRevApplied.15.034027.
  12. McArdle S., Gilyén A., Berta M. Quantum state preparation without coherent arithmetic // 2022. arxiv preprint arxiv:2210.14892.
  13. Zoufal C., Lucchi A., Woerner S. Quantum generative adversarial networks for learning and loading random distributions // Quantum Information. 2019. vol. 5(103). doi: 10.1038/s41534-019-0223-2.
  14. Li J., Kais S. A universal quantum circuit design for periodical functions // New Journal of Physics. 2021. vol. 23. doi: 10.1088/1367-2630/ac2cb4.
  15. Stamatopoulos N., Zeng W.J. Derivative Pricing using Quantum Signal Processing // 2023. arxiv preprint arxiv:2307.14310.
  16. Grover L.K. A fast quantum mechanical algorithm for database search // Proceedings of the Twenty Eighth Annual ACM Symposium on the Theory of Computing. 1996. pp. 212–219. doi: 10.1145/237814.237866.
  17. Vasicek O. An equilibrium characterization of the term structure // Journal of Financial Economics. 1977. vol. 5(2). pp. 177–188.
  18. Chakrabarti S., Krishnakumar R., Mazzola G., Stamatopoulos N., Woerner S., Zeng W.J. A Threshold for Quantum Advantage in Derivative Pricing // 2021. Quantum 5. vol. 463. doi: 10.22331/q-2021-06-01-463.
  19. Castelvecchi D. IBM releases first-ever 1,000-qubit quantum chip, 2023. URL: https://www.nature.com/articles/d41586-023-03854-1 (accessed 18.09.2024).
  20. IBM. Charting the course to 100,000 qubits. 2023. URL: https://research.ibm.com/blog/100k-qubit-supercomputer. (accessed 18.09.2024).
  21. Боряев Р.О., Чуваков А.В. Квантовые вычисления в автоматизированных системах управления факторинговыми операциями // Вестник СамГТУ, Технические науки. 2023. № 2(78). С. 6–19.
  22. Боряев Р.О., Чуваков А.В. Необходимость использования квантовых вычислений в автоматизированных системах управления факторинговыми операциями // Сборник материалов IX Международной научно-практической конференции (школы-семинара) молодых ученых (г. Тольятти, 18-20 апреля). 2023. С. 407–412.
  23. AWS Cloud Quantum Computing Service. IQM, 2024. URL: https://aws.amazon.com/ru/braket/quantum-computers/iqm/. (accessed 18.09.2024).

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