Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds - Muhamad Akrom; Supriadi Rustad; Totok Sutojo; Wahyu Aji Eko Prabowo; Hermawan Kresno Dipojono; Ryo Maezono; Hideaki Kasai (Results in Surfaces and Interfaces)
This study introduces the stacking classical-quantum model (SCQM) as a novel approach to predicting N-heterocyclic compounds' corrosion inhibition efficiency (CIE). SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with Quantum Neural Network (QNN) as the meta-learner. Experimental results demonstrate SCQM's superior performance with a coefficient of determination (R2) value of 0.98 and root mean squared error (RMSE) of 0.92, outperforming classical models. Predictions of new derivatives PP1 and PP2 yielded CIE values of 95.39% and 94.05%, aligning with experimental results. The novelty lies in the hybrid framework's ability to leverage quantum feature maps, offering a groundbreaking method to explore anti-corrosion materials through quantum machine learning (QML).