Citation

Dutta, Pijush and Deb, Pradip and Nagpal, Pooja and Naskar, Shantanu and Mondal, Anubrata and Tan, Yi Fei and Chua, Fang Fang Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials. Journal of Polymer & Composites, 14 (5). pp. 19-35. ISSN 2321-2810

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Abstract

In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum–Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test the proposed model, 420 samples, including shear rate, temperature, and composition-related parameters, were employed. Statistical metrics – like MSE, RMSE, MAE, and were used to evaluate predictive performance. The results show that both quantum models outperform the classical machine learning approaches, where the QNN has the highest prediction accuracy ( = 0.970) with RMSE = 0.0435 and MAE = 0.0324 for the testing set, and also has a very good generalization capacity. Moreover, a comparative study was also performed on this model for validation purposes. A sensitivity analysis indicated Polymer concentration and temperature as the key parameters determining the viscosity behavior. The results prove the potential of hybrid quantum–classical frameworks as powerful and data-rich tools for rheological modeling, the intelligent optimization of manufacturing processes, and future developments in materials informatics in Industry 5.0.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	01 Oct 2026 08:05
Last Modified:	01 Oct 2026 08:07
URII:	http://shdl.mmu.edu.my/id/eprint/16799

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