Development of learning models based on machine learning with quantum annealing for learning optimization in the digital era

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.333721

Keywords:

machine learning, hyperparameter tuning, complexity of quantum annealing, optimization digital transformation

Abstract

The object of this study is the prediction of digital learning achievement. The problems solved in this study are the low accuracy and efficiency of the prediction model caused by the complexity of the learning data and the limitations of conventional tuning methods such as grid search and random search which are unable to optimally navigate the wide and non-linear parameter space. The results obtained show that the integration of quantum annealing into the hyperparameter optimization process can significantly improve model performance. Model accuracy increased from 82% to 91%, with consistent improvements in precision, recall, and F1-score. The model also showed faster convergence and lower losses on both training and testing data, indicating better generalization capabilities to new data. Interpretation of these results concludes that quantum annealing can navigate the parameter space efficiently, exploring combinations of values that are unreachable by conventional methods. The main feature and characteristic of these results lies in its ability to combine the computational efficiency of LightGBM with the exploration of complex solutions through quantum methods, making it very suitable for dynamic learning problems. The scope and conditions of practical use of the developed model include digital-based learning management systems, adaptive learning platforms. These findings are relevant to be applied in the development of artificial intelligence-based education systems that support personalization in the current era of digital transformation

Author Biographies

Irfan Dahnial, Universitas Muhammadiyah Sumatera Utara

Doctor of Elementary Education

Department of Education

Al-Khowarizmi Al-Khowarizmi, Universitas Muhammadiyah Sumatera Utara

Doctor of Computer Science

Department of Information Technology

Karina Winda, Universitas Muhammadiyah Sumatera Utara

Master of Elementary Education

Department of Education

References

  1. Yulianti, L. P., Surendro, K. (2022). Implementation of Quantum Annealing: A Systematic Review. IEEE Access, 10, 73156–73177. https://doi.org/10.1109/access.2022.3188117
  2. Salloum, H., Aldaghstany, H. S., Orabi, O., Haidar, A., Bahrami, M. R., Mazzara, M. (2024). Integration of Machine Learning with Quantum Annealing. Advanced Information Networking and Applications, 338–348. https://doi.org/10.1007/978-3-031-57870-0_30
  3. Nath, R. K., Thapliyal, H., Humble, T. S. (2021). A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications. SN Computer Science, 2 (5). https://doi.org/10.1007/s42979-021-00751-0
  4. Salloum, H., Sabbagh, K., Savchuk, V., Lukin, R., Orabi, O., Isangulov, M., Mazzara, M. (2025). Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets. IEEE Access, 13, 16263–16287. https://doi.org/10.1109/access.2025.3531391
  5. Jooya, A., Keshavarz, B., Dimopoulos, N., Oberoi, J. S. (2017). Accelerating Neural Network Ensemble Learning Using Optimization and Quantum Annealing Techniques. Proceedings of the Second International Workshop on Post Moores Era Supercomputing, 1–7. https://doi.org/10.1145/3149526.3149528
  6. Zielewski, M., Takahashi, K., Shimomura, Y., Takizawa, H. (2023). Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning. IEEE Access, 11, 104285–104294. https://doi.org/10.1109/access.2023.3317698
  7. Wang, H., Wang, W., Liu, Y., Alidaee, B. (2022). Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection. IEEE Access, 10, 75908–75917. https://doi.org/10.1109/access.2022.3190897
  8. Panizza, V., Hauke, P., Micheletti, C., Faccioli, P. (2024). Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization. arXiv. https://doi.org/10.48550/arXiv.2407.07177
  9. Pranjic, D., Mummaneni, B. C., Tutschku, C. (2024). Quantum Annealing based Feature Selection in Machine Learning. arXiv. https://doi.org/10.48550/arXiv.2411.19609
  10. Yoon, B., Chang, C. C., Kenyon, G. T., Nguyen, N. T. T., Rrapaj, E. (2022). Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer. Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021), 143. https://doi.org/10.22323/1.396.0143
  11. Yulianti, L. P., Trisetyarso, A., Santoso, J., Surendro, K. (2023). A hybrid quantum annealing method for generating ensemble classifiers. Journal of King Saud University - Computer and Information Sciences, 35 (10), 101831. https://doi.org/10.1016/j.jksuci.2023.101831
  12. Setiadi, D. R. I. M., Susanto, A., Nugroho, K., Muslikh, A. R., Ojugo, A. A., Gan, H.-S. (2024). Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model. Computers, 13 (8), 191. https://doi.org/10.3390/computers13080191
  13. Felefly, T., Roukoz, C., Fares, G., Achkar, S., Yazbeck, S., Meyer, P. et al. (2023). An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection. Journal of Digital Imaging, 36 (6), 2335–2346. https://doi.org/10.1007/s10278-023-00886-x
  14. Okey, O. D., Maidin, S. S., Lopes Rosa, R., Toor, W. T., Carrillo Melgarejo, D., Wuttisittikulkij, L. et al. (2022). Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks. Sustainability, 14 (23), 15901. https://doi.org/10.3390/su142315901
  15. Samà, M., D’Ariano, A., D’Ariano, P., Pacciarelli, D. (2017). Scheduling models for optimal aircraft traffic control at busy airports: Tardiness, priorities, equity and violations considerations. Omega, 67, 81–98. https://doi.org/10.1016/j.omega.2016.04.003
  16. Jamili, A. (2017). A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem. Journal of Air Transport Management, 58, 21–30. https://doi.org/10.1016/j.jairtraman.2016.08.008
  17. Zhang, L., Li, Z., Królczyk, G., Wu, D., Tang, Q. (2019). Mathematical modeling and multi-attribute rule mining for energy efficient job-shop scheduling. Journal of Cleaner Production, 241, 118289. https://doi.org/10.1016/j.jclepro.2019.118289
  18. Correa Issi, G., Linfati, R., Escobar, J. W. (2020). Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area. Journal of Advanced Transportation, 2020, 1–13. https://doi.org/10.1155/2020/8813372
  19. Hammad, A. W., Grzybowska, H., Sutrisna, M., Akbarnezhad, A., Haddad, A. (2019). A novel mathematical optimisation model for the scheduling of activities in modular construction factories. Construction Management and Economics, 38 (6), 534–551. https://doi.org/10.1080/01446193.2019.1682174
  20. Aly, M. (2024). Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model. Multimedia Tools and Applications, 84 (13), 12575–12614. https://doi.org/10.1007/s11042-024-19392-5
  21. Rizwan, S., Nee, C. K., Garfan, S. (2025). Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC Using Deep Learning: A Systematic Literature Review. IEEE Access, 13, 18952–18982. https://doi.org/10.1109/access.2025.3533915
Development of learning models based on machine learning with quantum annealing for learning optimization in the digital era

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Published

2025-06-30

How to Cite

Dahnial, I., Al-Khowarizmi, A.-K., & Winda, K. (2025). Development of learning models based on machine learning with quantum annealing for learning optimization in the digital era. Eastern-European Journal of Enterprise Technologies, 3(2 (135), 65–72. https://doi.org/10.15587/1729-4061.2025.333721

Issue

Vol. 3 No. 2 (135) (2025): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2025 Irfan Dahnial, Al-Khowarizmi Al-Khowarizmi, Karina Winda

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