AUT Journal of Modeling and Simulation

AUT Journal of Modeling and Simulation

A Stability-Guided Quantum Feature Selection and Quantum Kernel SVM Framework for Entrepreneurial Competency Prediction

Document Type : Research Article

Authors
Mohammad Reza Mehregan
Arman Rezasoltani
Amir Mohammad Khani
Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
10.22060/miscj.2026.25629.5484
Abstract
The key challenges of performance prediction in entrepreneurship stem from the heterogeneous, multidimensional, nonlinear, and imbalanced characteristics of entrepreneurial behaviors, i.e., ambitiousness and risk preference. While machine learning algorithms, including Support Vector Machines, Random Forests, XGBoost, and LightGBM, have been purported and adopted to model entrepreneurial outcomes, their optimization saturation effect in performance prediction between 60% and 79% is observed, with little novelty in representational expansion by resampling and ensemble engineering approaches. To overcome the limits of existing models, this paper adopted a stability-guided quantum-enhanced hierarchical QAOA-QSVM framework whereby hierarchical Quantum Approximate Optimization Algorithm (QAOA) feature selection interacted with Quantum Kernel Support Vector Machine for Entrepreneurial Skill (QSVM). Features extracted from 219 university students' gender, race, personality, and university attributes were successively selected by formulating the QUBO problem of feature optimization and solving with QAOA. The stability consensus was weighed by stratified five-folds to improve reproducibility and robustness. Selected features were eventually embedded in an entangled quantum Hilbert space of the ZZFeatureMap to enable high-order nonlinear feature interactions with quantum kernel learners. The results indicated that our quantum approach surpassed a classical RBF-SVM on information from university students by nearly 22% (83.09% vs. 60.68%) and reached an 86.84% recall for entrepreneur prediction. We conclude that quantum representation learning structures can provide more insightful feature embedding ways, enabling novel transformers in talent pipeline building of entrepreneurship courses. This study also extends a meta-complementary theory between quantum machine learning and entrepreneurship.
Keywords
Quantum Machine Learning
Quantum Feature Selection
Quantum Approximate Optimization Algorithm
Entrepreneurial Competency Prediction
Subjects
  1. Abedin, T., Xu, H., & Uddin, S. (2026). The impact of K selection in K‑fold cross-validation on bias and variance in supervised learning models. Scientific Reports, 16(1), 6084. https://doi.org/10.1038/s41598-026-37247-x
  2. Adeel, S., Daniel, A. D., & Botelho, A. (2023). The effect of entrepreneurship education on the determinants of entrepreneurial behaviour among higher education students: A multi-group analysis. Journal of Innovation & Knowledge, 8(1), 100324. https://doi.org/10.1016/j.jik.2023.100324
  3. Allgaier, J., & Pryss, R. (2024). Cross-Validation Visualized: A narrative guide to advanced methods. Machine Learning and Knowledge Extraction, 6(2), 1378–1388. https://doi.org/10.3390/make6020065
  4. Al-Qadasi, N., Zhang, G., Al-Awlaqi, M. A., Alshebami, A. S., & Aamer, A. (2023). Factors influencing entrepreneurial intention of university students in Yemen: The mediating role of entrepreneurial self-efficacy. Frontiers in Psychology, 14, 1111934. https://doi.org/10.3389/fpsyg.2023.1111934
  5. Ayachi, F. E., & Baz, M. E. (2025). Enhancing quantum support vector machines using multipartite entanglement. Physics Letters A, 551, 130666. https://doi.org/10.1016/j.physleta.2025.130666
  6. Bansal, A. (2025). Quantum algorithms for optimization. IOSR Journal of Computer Engineering, 27(5), 07–26. https://doi.org/10.9790.0661-2705050726
  7. Bhatt, P. C., Drave, V. A., Hsu, Y., & Lai, K. (2025). Explainable artificial intelligence approach to predict student entrepreneurial competency. In Communications in Computer and Information Science (pp. 99–113). https://doi.org/10.1007.978-3-031-82150-9_9
  8. Botha, M., & Taljaard, A. (2021). Exploring the Entrepreneurial Intention-Competency Model for Nascent Entrepreneurs: Insights from a Developing Country Context. Frontiers in Psychology, 12, 516120. https://doi.org/10.3389/fpsyg.2021.516120
  9. Cao, Y., Asad, M. M., Wang, L., Naz, A., & Almusharraf, N. (2022). Role of personality traits for entrepreneurial intentions of young entrepreneurs: A case study of a higher education institution. Frontiers in Psychology, 13, 1010412. https://doi.org/10.3389/fpsyg.2022.1010412
  10. Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., & Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644. https://doi.org/10.1038/s42254-021-00348-9
  11. Corli, S., Moro, L., Dragoni, D., Dispenza, M., & Prati, E. (2024). Quantum machine learning algorithms for anomaly detection: A review. Future Generation Computer Systems, 166, 107632. https://doi.org/10.1016/j.future.2024.107632
  12. Dai, W., & Li, S. (2025). A model for evaluating and predicting college students’ innovation and entrepreneurship capabilities based on deep learning. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00602-4
  13. Devadas, R. M., & T, S. (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
  14. Díez-Valle, P., Porras, D., & García-Ripoll, J. J. (2024). Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling. Frontiers in Quantum Science and Technology, 3. https://doi.org/10.3389/frqst.2024.1321264
  15. Duong, C. D. (2023). Entrepreneurship: Nature, nurture, or both? Empirical evidence from a moderated polynomial regression with response surface analysis. The International Journal of Management Education, 21(3), 100877. https://doi.org/10.1016/j.ijme.2023.100877
  16. Figgatt, C., Maslov, D., Landsman, K. A., Linke, N. M., Debnath, S., & Monroe, C. (2017). Complete 3-Qubit Grover search on a programmable quantum computer. Nature Communications, 8(1), 1918. https://doi.org/10.1038/s41467-017-01904-7
  17. Grange, C., Poss, M., & Bourreau, E. (2024). An introduction to variational quantum algorithms for combinatorial optimization problems. Annals of Operations Research, 343(2), 847–884. https://doi.org/10.1007/s10479-024-06253-5
  18. Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212. https://doi.org/10.1038/s41586-019-0980-2
  19. Jafarnjad, A., Rezasoltani, A., and Khani, A. M. (2025). Analyzing and Predicting Hiring Decisions Using Machine Learning and Deep Learning. Journal of Public Administration, 17(2), 295-327. https://doi.org/10.22059/jipa.2025.390322.3649
  20. Jafarnejad, A., Rezasoltani, A., Khani, A. M., & Sayedeh Hoda, H. (2025). A hybrid feature selection and classification framework for predicting entrepreneurial competency using machine learning and binary grey wolf optimizer. Journal of Systems Thinking in Practice, 4(4), 129–155. https://doi.org/10.22067/jstinp.2025.94947.1172
  21. Jarial, S., & Verma, J. (2023). Prognosis of entrepreneurial traits among agricultural undergraduate students in India using machine learning. Journal of Agribusiness in Developing and Emerging Economies, 14(5), 905–919. https://doi.org/10.1108/jadee-06-2022-0124
  22. Jha, R. K., Kasabov, N., Bhattacharyya, S., Coyle, D., & Prasad, G. (2026). Comparative performance analysis of quantum feature maps for quantum kernel-based machine learning. Scientific Reports. https://doi.org/10.1038/s41598-026-39392-9
  23. Kaur, S., & Bhatt, P. C. (2023). Forecasting Student Entrepreneurial Competency: Machine Learning Approach. 2023 IEEE 3rd International Conference on Social Sciences and Intelligence Management (SSIM), 10, 184–189. https://doi.org/10.1109/ssim59263.2023.10469640
  24. Khani, A. M., Mohaghar, A., Rezasoltani, A., & Hosseinian, S. H. (2025). Advanced hyperparameter optimization and adaptive synthetic sampling in machine learning for predictive maintenance of industrial machinery. International journal of research in industrial engineering, 14(4), 607-629. https://doi.org/10.22105/riej.2025.500994.1528
  25. Koç, Ş., Uctu, R., Essop, H., & Mercan, B. (2025). The impact of innovation on economic growth: A dynamic panel data analysis using the system GMM approach. International Journal of Innovation Studies, 10(1), 20–30. https://doi.org/10.1016/j.ijis.2025.09.003
  26. Kocak, B., Klontzas, M. E., Stanzione, A., Meddeb, A., Demircioğlu, A., Bluethgen, C., Bressem, K. K., Ugga, L., Mercaldo, N., Díaz, O., & Cuocolo, R. (2025). Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations. European Journal of Radiology Artificial Intelligence, 3, 100030. https://doi.org/10.1016/j.ejrai.2025.100030
  27. Kraus, S., McDowell, W., Ribeiro-Soriano, D. E., & Rodríguez-García, M. (2021). The role of innovation and knowledge for entrepreneurship and regional development. Entrepreneurship and Regional Development, 33(3–4), 175–184. https://doi.org/10.1080.22797254.2021.1872929
  28. Lee, K., & Yun, Y. (2020). Reconciling Resource-Based view and Competency-Based view of Start-Up formation in universities. IEEE Access, 8, 143274–143284. https://doi.org/10.1109/access.2020.3014213
  29. Martínez-Martínez, S. L., Ventura, R., & Santos-Jaén, J. M. (2025). Creativity Vs Grit: key competences to understand Entrepreneurial Intention. The International Journal of Management Education, 23(2), 101143. https://doi.org/10.1016/j.ijme.2025.101143
  30. Monzón-Verona, J. M., García-Alonso, S., & Santana-Martín, F. J. (2025). Quantum Variational vs. Quantum Kernel Machine Learning Models for Partial Discharge Classification in Dielectric Oils. Sensors, 25(4), 1277. https://doi.org/10.3390/s25041277
  31. Morales, M. E. S., Biamonte, J., & Zimborás, Z. (2019). On the Universality of the Quantum Approximate Optimization Algorithm. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1909.03123
  32. Morris, T. H., & König, P. D. (2020). Self-directed experiential learning to meet ever-changing entrepreneurship demands. Education + Training, 63(1), 23–49. https://doi.org/10.1108/et-09-2019-0209
  33. Nwosu, H. E., Okolie, U. C., Awotoye, Y. F., Ejime, E. O., Ehiobuche, C., & Nkanta, I. (2025). Exploring the role of university entrepreneurial ecosystem in shaping conditional student entrepreneurship. Industry and Higher Education. https://doi.org/10.1177.09504222251388185
  34. Park, J. (2022). Estimating quantum mutual information of Continuous-Variable quantum states by measuring purity and covariance matrix. Entropy, 24(7), 940. https://doi.org/10.3390/e24070940
  35. Peral-García, D., Cruz-Benito, J., & García-Peñalvo, F. J. (2024). Systematic literature review: Quantum machine learning and its applications. Computer Science Review, 51, 100619. https://doi.org/10.1016/j.cosrev.2024.100619
  36. Petrylaite, E., & Rusk, M. (2020). Entrepreneurial marketing learning styles used by entrepreneurial teams. Journal of Research in Marketing and Entrepreneurship, 23(1), 60–85. https://doi.org/10.1108/jrme-12-2019-0099
  37. Priyadharshni, S., & Ravi, V. (2025). Dynamic graph-based quantum feature selection for accurate fetal plane classification in ultrasound imaging. Scientific Reports, 15(1), 41743. https://doi.org/10.1038/s41598-025-26835-y
  38. Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1), 6086. https://doi.org/10.1038/s41598-024-56706-x
  39. Raseena, V. (2025). Quantum computing: foundations, algorithms, and emerging applications. Frontiers in Quantum Science and Technology, 4. https://doi.org/10.3389/frqst.2025.1723319
  40. Reis, D. A., Fleury, A. L., & Carvalho, M. M. (2020). Consolidating core entrepreneurial competences: toward a meta-competence framework. International Journal of Entrepreneurial Behaviour & Research, 27(1), 179–204. https://doi.org/10.1108/ijebr-02-2020-0079
  41. Ruan, Y., Yuan, Z., Xue, X., & Liu, Z. (2022). Quantum approximate optimization for combinatorial problems with constraints. Information Sciences, 619, 98–125. https://doi.org/10.1016/j.ins.2022.11.020
  42. Sahid, S., Norhisham, N. S., & Narmaditya, B. S. (2024). Interconnectedness between entrepreneurial self-efficacy, attitude, and business creation: A serial mediation of entrepreneurial intention and environmental factors. Heliyon, 10(9), e30478. https://doi.org/10.1016/j.heliyon.2024.e30478
  43. Sharma, U., & Manchanda, N. (2020). Predicting and Improving Entrepreneurial Competency in University Students using Machine Learning Algorithms. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2, 305–309. https://doi.org/10.1109/confluence47617.2020.9058292
  44. Sieg, P., Posadzińska, I., & Jóźwiak, M. (2023). Academic entrepreneurship as a source of innovation for sustainable development. Technological Forecasting and Social Change, 194, 122695. https://doi.org/10.1016/j.techfore.2023.122695
  45. Şimşek, M., & Daş, A. S. (2022). The effect of handling imbalanced datasets methods on prediction of entrepreneurial competency in university students. Turkish Journal of Forecasting, 06(2), 53–60. https://doi.org/10.34110/forecasting.1185545
  46. Somià, T., Pittaway, L., & Benedict, P. (2024). Rethinking entrepreneurial competencies: A gender-focused analysis of students’ perceived competencies. The International Journal of Management Education, 22(3), 101038. https://doi.org/10.1016/j.ijme.2024.101038
  47. Sun, X., & Usman, M. A. (2024). Pixels to Paychecks: Understanding the Determinants of Latent Entrepreneurial Transition in a Digital Context. SAGE Open, 14(4). https://doi.org/10.1177.21582440241288928
  48. Suryadi, D., & Kurniawan, D. (2024). Predicting Entrepreneurial Spirit: A Machine Learning Approach. 2024 International Conference on Information Management and Technology (ICIMTech), 399–403. https://doi.org/10.1109/icimtech63123.2024.10780930
  49. Szeghalmy, S., & Fazekas, A. (2023). A comparative study of the use of stratified Cross-Validation and Distribution-Balanced stratified Cross-Validation in imbalanced learning. Sensors, 23(4), 2333. https://doi.org/10.3390/s23042333
  50. Thanasilp, S., Wang, S., Cerezo, M., & Holmes, Z. (2024). Exponential concentration in quantum kernel methods. Nature Communications, 15(1), 5200. https://doi.org/10.1038/s41467-024-49287-w
  51. Toufighi, S. P., Khani, A. M., Rezasoltani, A., Sahebi, I. G., & Vang, J. (2025). Forecasting stock market anomalies in emerging markets: An OPTUNA-optimized isolation forest and K-means approach. Machine Learning With Applications, 22, 100770. https://doi.org/10.1016/j.mlwa.2025.100770
  52. Van Gelderen, M. (2022). Developing entrepreneurial competencies through deliberate practice. Education + Training, 65(4), 530–547. https://doi.org/10.1108/et-10-2021-0396
  53. Vu, H. M., & Nwachukwu, C. (2021). Influence of entrepreneur competencies on profitability and employee satisfaction. International Journal of Management and Enterprise Development, 20(1), 1. https://doi.org/10.1504/ijmed.2021.113635
  54. Weidman, J. D., Sajjan, M., Mikolas, C., Stewart, Z. J., Pollanen, J., Kais, S., & Wilson, A. K. (2024). Quantum computing and chemistry. Cell Reports Physical Science, 5(9), 102105. https://doi.org/10.1016/j.xcrp.2024.102105. 
AUT Journal of Modeling and Simulation
Volume 58, Issue 1
2026
Pages 3-22