Corrosion inhibition efficiency (IE%) prediction plays a central role
in the computational discovery of high-performance organic
inhibitors. Classical machine learning has shown promising results;
however, its performance often deteriorates when learning non-linear
interactions between quantum chemical descriptors. Meanwhile,
quantum machine learning (QML) provides enhanced expressivity
through quantum feature mapping but remains limited by NISQ-era
hardware. In this study, we propose a Hybrid Quantum Neural
Network (HQNN) integrating classical dense layers with variational
quantum circuits (VQC) to predict the inhibition efficiency of organic
corrosion inhibitors. Using a curated dataset of 660 molecules with
DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of
0.958, outperforming classical regressors and pure VQC. The results
demonstrate that hybrid quantum models offer a balanced trade-off
between quantum advantage and practical feasibility in materials
informatics.