https://doi.org/10.1140/epjqt/s40507-025-00385-8
Research
Hybrid quantum neural networks with variational quantum regressor for enhancing QSPR modeling of CO2-capturing amine
1
The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine, Yonsei University, Incheon, Republic of Korea
2
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, Republic of Korea
Received:
1
April
2025
Accepted:
17
June
2025
Published online:
23
June
2025
Accurate amine property prediction is essential for optimizing CO2 capture efficiency in post-combustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference to capture complex correlations. In this study, we developed hybrid quantum neural networks (HQNN) to improve quantitative structure-property relationship (QSPR) modeling for CO2-capturing amines. By integrating variational quantum regressors with classical multi-layer perceptrons and graph neural networks, quantum-enhanced performance was explored in physicochemical property prediction under noiseless conditions and robustness was evaluated against quantum hardware noise using IBM quantum systems. Our results showed that HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure. The fine-tuned and frozen pre-trained HQNN models with 9 qubits consistently achieved the highest rankings, highlighting the benefits of integrating quantum layers with pre-trained classical models. Furthermore, simulations under hardware noise confirmed the robustness of HQNNs, maintaining predictive performance. Overall, these findings emphasize the potential of hybrid quantum-classical architectures in molecular modeling. As quantum hardware and QML algorithms continue to advance, practical quantum benefits in QSPR modeling and materials discovery are expected to become increasingly attainable, driven by improvements in quantum circuit design, noise mitigation, and scalable architectures.
Key words: Quantum machine learning / Quantum neural network / Quantitative structure-property relationship / Materials science / Amine-based carbon capture
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjqt/s40507-025-00385-8.
© The Author(s) 2025
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