Predictive Models Talented Researcher Using Modern Approach Quantum Machine Learning (QML) - Lukman Anas; Aghus Sofwan; Iwan Setiawan (ISB Atma Luhur, 2026)
Scientific advancement is profoundly shaped by the ability of exceptional individuals to investigate and produce novel insights. Nonetheless, conventional assessment techniques that depend on bibliometric metrics such as the Scopus Score, Sinta, Google Scholar (GS) , and the H-Index—frequently neglect to encapsulate the intricate dynamics associated with research quality. In order to rectify these inadequacies, this study introduces a model aimed at the identification of gifted researchers through a Quantum Machine Learning (QML) methodology. The proposed framework seeks to surmount the constraints of ranking systems that rely exclusively on scientometric indicators by incorporating a reconstructed kernel Hilbert space (RKHS). The research methodology is delineated into four principal phases: (1) data gathering and preprocessing, (2) QSVM model training, (3) researcher score identification and visualization, and (4) performance assessment by comparing actual and anticipated scores. QSVM was tested using a dataset of researchers from various fields. Results show that QSVM accurately predicts researcher performance, with variances between Scores for the whole thing range from -0.25 to 0.05. The plan that was offered congruence with actual performance data supports its robustness. The ranking analysis shows a low mistake rate, proving QSVM's academic performance evaluation accuracy. QML-based categorization models can be scalable and data-driven alternatives to standard research assessment methods, according to this study.