Exploring Contemporary Data Analysis Approaches Employing Quantum Algorithms

Authors

DOI:

https://doi.org/10.32515/2414-3820.2025.55.314-322

Keywords:

Data Mining, quantum algorithms, HHL, VQE, QAOA, Quantum PCA, hybrid computing, data analysis

Abstract

The article examines the integration of modern mathematical methods of data analysis with quantum technologies, forming a new interdisciplinary field – quantum analytics. The relationship between classical methods (PCA, SVM, k-means, Monte Carlo, and Least Squares Method) and their quantum counterparts (Quantum PCA, Quantum Kernels, q-means, QAE, HHL) is analyzed. A comparative table of efficiency and computational complexity is presented, demonstrating the potential for exponential or quadratic speedup when using quantum algorithms (e.g., O(log n) versus O(n³), O(1/ε) versus O(1/ε²)). Particular attention is given to the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) as practical tools for NISQ-devices. The study highlights the prospects of hybrid quantum-classical models for solving problems in big data analysis, optimization, and forecasting. The obtained results confirm that quantum methods can significantly reduce computation time and provide a foundation for the development of next-generation intelligent systems.

Author Biographies

Iryna Lysenko, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

PhD in Technical Sciences (Candidate of Technical Sciences), Senior Lecturer, Department of Cybersecurity and Software Engineering

Roman Minailenko, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

Associate Professor, PhD of technical sciences (Candidate of Technical Sciences), Associate Professor of the Department of Cybersecurity and Software Engineering

References

Список літератури

1. Devadas R. M., Sowmya T. Quantum Machine Learning: A Comprehensive Review of Integrating AI with Quantum Computing for Computational Advancements. MethodsX. 2025. Vol. 14. P. 103318. DOI: https://doi.org/10.1016/j.mex.2025.103318.

2. Smith A., Zhao L. Variational Quantum Algorithms for Semidefinite Programming . Quantum Journal. 2024. DOI: https://doi.org/10.22331/q-2024-06-17-1374.

3. Bittel L., Kliesch M. Training Variational Quantum Algorithms Is NP-Hard . Physical Review A. 2024. Vol. 109, №4. P. 042426. DOI: https://doi.org/10.1103/PhysRevA.109.042426.

4. Huang H.-Y., Kueng R., Preskill J. Information-Theoretic Bounds on Quantum Advantage in Machine Learning. Physical Review Letters. 2025. Vol. 125, №2. P. 020501. DOI: https://doi.org/10.1103/PhysRevLett.125.020501.

5. Cerezo M., Arrasmith A., Babbush R. Variational Quantum Algorithms: A Review . Nature Reviews Physics. 2025. Vol. 3. P. 625-644. DOI: https://doi.org/10.1038/s42254-025-00219-x.

6. Дорошенко А. В., Савчук Д. Ю. Дослідження методів інтелектуального аналізу даних для класифікації незбалансованих наборів даних. Український журнал інформаційних технологій. 2024. Т. 6, №1. С. 48–57. DOI: https://doi.org/10.23939/ujit2024.01.048.

7. Шевченко Г. Я. Data Mining для дослідницьких цілей. Наукові вісті НБУВ. 2025. №2. С. 27–35. DOI: https://doi.org/10.15407/sofs2025.02.027.

8. Селіворстова Т. В., Мала Ю. А., Гуда А. І. Використання технології Orange для інтелектуального аналізу даних в освітній галузі. Системні технології. 2024. Вип. 152. DOI: https://doi.org/10.34185/1562-9945-3-152-2024-12.

9. Лявинець Г. М., Губеня В. О., Люлька О. М., Ткачук Ю. М. Data Mining у адаптивному менеджменті готельно-ресторанного бізнесу. Інтернаука. Серія: Економічні науки. 2024. №11. URL: https://dspace.nuft.edu.ua/server/api/core/bitstreams/26c24d3d-490c-48ab-9515-c4a29ce765d0/content (дата звернення 25.10.2025)

10. Криниця С., Гордей О., Коваленко Ю., Данькевич А., Болдов А. Використання технологій Big Data для посилення участі громадськості в управлінні публічними фінансами. Financial and Credit Activity: Problems of Theory and Practice. 2024. Т. 3, №56. С. 186–203. DOI: https://doi.org/10.55643/fcaptp.3.56.2024.4402.

11. Knopov P. S., Pepelyaeva T. V. Controlled Stochastic Systems. Cybernetics and Systems Analysis. 2024. Vol. 60. P. 525–540. DOI: https://doi.org/10.1007/s10559-024-00693-4.

12. Vlasenko L. A. Stochastic Descriptor Pursuit Game. Cybernetics and Systems Analysis. 2024. Vol. 60. P. 433–441. DOI: https://doi.org/10.1007/s10559-024-00684-5.

References

1. Devadas, R. M., & Sowmya, T. (2025). Quantum Machine Learning: A Comprehensive Review of Integrating AI with Quantum Computing for Computational Advancements. MethodsX, 14, 103318. https://doi.org/10.1016/j.mex.2025.103318

2. Smith, A., & Zhao, L. (2024). Variational Quantum Algorithms for Semidefinite Programming. Quantum Journal. https://doi.org/10.22331/q-2024-06-17-1374

3. Bittel, L., & Kliesch, M. (2024). Training Variational Quantum Algorithms Is NP-Hard. Physical Review A, 109(4), 042426. https://doi.org/10.1103/PhysRevA.109.042426

4. Huang, H.-Y., Kueng, R., & Preskill, J. (2025). Information-Theoretic Bounds on Quantum Advantage in Machine Learning. Physical Review Letters, 125(2), 020501. https://doi.org/10.1103/PhysRevLett.125.020501

5. Cerezo, M., Arrasmith, A., & Babbush, R. (2025). Variational Quantum Algorithms: A Review. Nature Reviews Physics, 3, 625–644. https://doi.org/10.1038/s42254-025-00219-x

6. Doroshenko, A. V., & Savchuk, D. Yu. (2024). Research of Data Mining Methods for Classification of Imbalanced Data Sets. Ukrainskyi Zhurnal Informatsiinykh Tekhnolohii, 6(1), 48–57. https://doi.org/10.23939/ujit2024.01.048 [in Ukrainian].

7. Shevchenko, H. Ya. (2025). Data Mining for Research Purposes. Naukovi Visti NBUV, (2), 27–35. https://doi.org/10.15407/sofs2025.02.027 [in Ukrainian].

8. Selivorstova, T. V., Mala, Yu. A., & Huda, A. I. (2024). Using orange technology for data mining in the education sector. Systemni Tekhnolohii, (152). https://doi.org/10.34185/1562-9945-3-152-2024-12 [in Ukrainian].

9. Liavynets, H. M., Hubenia, V. O., Liulka, O. M., & Tkachuk, Yu. M. (2024). Data Mining in Adaptive Management of Hotel and Restaurant Business. Seriia: Ekonomichni Nauky, (11). Retrieved from https://dspace.nuft.edu.ua/server/api/core/bitstreams/26c24d3d-490c-48ab-9515-c4a29ce765d0/content [in Ukrainian].

10. Krynytsia, S., Hordei, O., Kovalenko, Yu., Dankevych, A., & Boldov, A. (2024). Leveraging Big Data Technologies for Enhanced Public Participation in Public Financial Management. Financial and Credit Activity: Problems of Theory and Practice, 3(56), 186–203. https://doi.org/10.55643/fcaptp.3.56.2024.4402 [in Ukrainian].

11. Knopov, P. S., & Pepelyaeva, T. V. (2024). Controlled Stochastic Systems. Cybernetics and Systems Analysis, 60, 525–540. https://doi.org/10.1007/s10559-024-00693-4

12. Vlasenko, L. A. (2024). Stochastic Descriptor Pursuit Game. Cybernetics and Systems Analysis, 60, 433–441. https://doi.org/10.1007/s10559-024-00684-5

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Published

2025-12-23

How to Cite

Lysenko, I., & Minailenko, R. (2025). Exploring Contemporary Data Analysis Approaches Employing Quantum Algorithms. Design, Production and Exploitation of Agricultural Machines, 55, 314–322. https://doi.org/10.32515/2414-3820.2025.55.314-322

Issue

Vol. 55 (2025): Design, Production and Exploitation of Agricultural Machines

Section

COMPUTER SCIENCE

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Copyright (c) 2025 Iryna Lysenko, Roman Mynailenko

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This work is licensed under a Creative Commons Attribution 4.0 International License.