DOI: https://doi.org/10.32515/2414-3820.2025.55.314-322
Exploring Contemporary Data Analysis Approaches Employing Quantum Algorithms
About the Authors
Iryna Lysenko, PhD in Technical Sciences (Candidate of Technical Sciences), Senior Lecturer, Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4394-4960, e-mail: min_max@i.ua
Roman Minailenko, Associate Professor, PhD of technical sciences (Candidate of Technical Sciences), Associate Professor of the Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0000-0563-0798, e-mail: aron70@ukr.net
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.
Keywords
Data Mining, quantum algorithms, HHL, VQE, QAOA, Quantum PCA, hybrid computing, data analysis
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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
Citations
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: 10.1016/j.mex.2025.103318.
2. Smith A., Zhao L. Variational Quantum Algorithms for Semidefinite Programming . Quantum Journal. 2024. DOI: 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: 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: 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: 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: 10.23939/ujit2024.01.048.
7. Шевченко Г. Я. Data Mining для дослідницьких цілей. Наукові вісті НБУВ. 2025. №2. С. 27–35. DOI: 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: 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: 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: 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: 10.1007/s10559-024-00684-5.
Copyright (c) 2025 Iryna Lysenko, Roman Mynailenko
Exploring Contemporary Data Analysis Approaches Employing Quantum Algorithms
About the Authors
Iryna Lysenko, PhD in Technical Sciences (Candidate of Technical Sciences), Senior Lecturer, Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4394-4960, e-mail: min_max@i.ua
Roman Minailenko, Associate Professor, PhD of technical sciences (Candidate of Technical Sciences), Associate Professor of the Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0000-0563-0798, e-mail: aron70@ukr.net
Abstract
Keywords
Full Text:
PDFReferences
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
Citations
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: 10.1016/j.mex.2025.103318.
2. Smith A., Zhao L. Variational Quantum Algorithms for Semidefinite Programming . Quantum Journal. 2024. DOI: 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: 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: 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: 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: 10.23939/ujit2024.01.048.
7. Шевченко Г. Я. Data Mining для дослідницьких цілей. Наукові вісті НБУВ. 2025. №2. С. 27–35. DOI: 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: 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: 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: 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: 10.1007/s10559-024-00684-5.