The Hybrid QGNN–QSVM (H-QGQS) Framework for the Prediction of University Publication Performance

Lukman Anas; Aghus Sofwan; Iwan Setiawan

doi:10.48084/etasr.17650

Authors

Lukman Anas Doctoral Program of Information Systems, Postgraduate School, Universitas Diponegoro, Semarang, Indonesia | Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
Aghus Sofwan Department of Electrical Engineering, Universitas Diponegoro, Semarang, Indonesia
Iwan Setiawan Department of Electrical Engineering, Universitas Diponegoro, Semarang, Indonesia

Volume: 16 | Issue: 3 | Pages: 34869-34878 | June 2026 | https://doi.org/10.48084/etasr.17650

Received: 19 January 2026 | Revised: 7 March 2026 and 20 March 2026 | Accepted: 21 March 2026 | Online: 5 April 2026

Corresponding author: Lukman Anas

Abstract

Assessing university publication performance using scientometric indicators is challenging due to the relational and non-linear nature of scholarly data. This study proposes a hybrid Quantum Graph Neural Network and Quantum Support Vector Machine (QGNN–QSVM) framework for graph-based classification of university publication performance into Low, Medium, and High categories. The framework integrates graph representation learning and quantum-enhanced classification through multiple experimental scenarios, progressing from classical machine learning models to a fully quantum-enhanced learning approach. Experimental results show that the classical Support Vector Machine (SVM) baseline achieved an accuracy of 0.92 on a balanced dataset of 3,818 records, whereas the proposed QGNN–QSVM framework achieved an accuracy of 0.73. The results indicate that while the classical SVM achieves higher overall accuracy, incorporating graph structures and quantum-based embeddings provides more expressive representations of the complex scientometric relationships. In particular, the proposed framework demonstrates strong capability in identifying high-performing universities, which is the most critical category in institutional performance evaluation. These findings suggest that quantum-enhanced graph learning offers a promising alternative for modeling complex scientometric data under current near-term quantum computing constraints. The study also highlights existing limitations and suggests future research directions, including architectural optimization, feature enrichment, and scalability improvements.

Keywords:

Quantum Machine Learning (QML), Graph Neural Networks (GNNs), scientometric analysis, Quantum Support Vector Machine (QSVM), institutional performance

Downloads

Download data is not yet available.

References

F. Mayta-Tovalino et al., "Scientometric evaluation of trends and global characteristics of published research on occupational public health," Heliyon, vol. 8, no. 12, Dec. 2022, Art. no. e12165.

N. Scelles and J. A. Teixeira da Silva, "Making the impact of publications within a field comparable by improving the field-weighted citation impact (FWCI): the case of sport management," Scientometrics, vol. 130, no. 3, pp. 1571–1586, Mar. 2025.

C. García-Villar and J. M. García-Santos, "Bibliometric indicators to evaluate scientific activity," Radiología (English Edition), vol. 63, no. 3, pp. 228–235, May 2021.

F. Emmert-Streib, S. Tripathi, A. Farea, and O. Yli-Harja, "Advanced Scientometric Analysis of Scientific Machine Learning and PINNs: Topic Modeling and Trend Analysis," IEEE Access, vol. 12, pp. 153253–153272, 2024.

M. Daradkeh, L. Abualigah, S. Atalla, and W. Mansoor, "Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics," Electronics, vol. 11, no. 13, June 2022, Art. no. 2066.

P. Lamichhane and D. B. Rawat, "Quantum Machine Learning: Recent Advances, Challenges, and Perspectives," IEEE Access, vol. 13, pp. 94057–94105, 2025.

F. Rodríguez-Díaz, D. Gutiérrez-Avilés, A. Troncoso, and F. Martínez-Álvarez, "A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications," ACM Computing Surveys, vol. 58, no. 4, Oct. 2025, Art. no. 91.

D. Ranga, A. Rana, S. Prajapat, P. Kumar, K. Kumar, and A. V. Vasilakos, "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions," Mathematics, vol. 12, no. 21, Oct. 2024, Art. no. 3318.

L. Anas, A. Sofwan, and I. Setiawan, "Talented Researcher Identification Model Based on Scientometric Indicators with Quantum Machine Learning (QML) Approach," in International Conference on Data Analytics and Management, London, UK, 2025, pp. 340–350.

L. Xu, X. Zhang, M. Li, and S. Shen, "Quantum support vector machine for multi classification," Communications in Theoretical Physics, vol. 76, no. 7, June 2024, Art. no. 075105.

D. Alaminos, M. B. Salas, and M. A. Fernández-Gámez, "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, vol. 59, no. 2, pp. 803–829, Feb. 2022.

J. Zhou et al., "Graph neural networks: A review of methods and applications," AI Open, vol. 1, pp. 57–81, Jan. 2020.

X.-M. Zhang, L. Liang, L. Liu, and M.-J. Tang, "Graph Neural Networks and Their Current Applications in Bioinformatics," Frontiers in Genetics, vol. 12, July 2021, Art. no. 690049.

G. Verdon, T. McCourt, S. Leichenauer, E. Luzhinca, V. Singh, and J. Hidary, "Quantum Graph Neural Networks," in Second Workshop on Machine Learning and the Physical Sciences, Vancouver, Canada, 2019.

S. Piperno, A. Ceschini, S. Y. Chang, M. Grossi, S. Vallecorsa, and M. Panella, "A study on quantum graph neural networks applied to molecular physics," Physica Scripta, vol. 100, no. 6, May 2025, Art. no. 065126.

N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, vol. 133, pp. 285–296, Sept. 2021.

R. Kumar, "Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence," Spectrum of Engineering and Management Sciences, vol. 3, no. 1, pp. 45–62, Jan. 2025.

Y. Dong, R. A. Johnson, and N. V. Chawla, "Can Scientific Impact Be Predicted?," IEEE Transactions on Big Data, vol. 2, no. 1, pp. 18–30, Mar. 2016.

Y. A. Alohali, M. S. Fayed, T. Mesallam, Y. Abdelsamad, F. Almuhawas, and A. Hagr, "A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field," BioMed Research International, vol. 2022, July 2022, Art. no. 2239152.

V. M. Venkatappa and Venkateshappa, "A Novel Quantum Convolution Neural Network for Image Classification Applications," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 27046–27051, Oct. 2025.

T. Hoang, "GNN: Graph Neural Network and Large Language Model for Data Discovery." arXiv, Aug. 27, 2024.

B. Khemani, S. Patil, K. Kotecha, and S. Tanwar, "A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions," Journal of Big Data, vol. 11, no. 1, Jan. 2024, Art. no. 18.

J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, "Quantum machine learning," Nature, vol. 549, no. 7671, pp. 195–202, Sept. 2017.

M. Schuld, I. Sinayskiy, and F. Petruccione, "An introduction to quantum machine learning," Contemporary Physics, vol. 56, no. 2, pp. 172–185, Apr. 2015.

T. Yin, "Quantum support vector machines: theory and applications," Theoretical and Natural Science, vol. 51, pp. 34–42, Nov. 2024.

D. Sharma, V. B. Sabale, P. Singh, and A. Kumar, "Harnessing quantum support vector machines for cross-domain classification of quantum states," Quantum Machine Intelligence, vol. 7, no. 1, Apr. 2025, Art. no. 49.