Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions.

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an R² of 0.96 and a MAE of 2.47 kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. A correlation analysis showed that the quantum-encoded representations have a stronger positive correlation with the target values than the original features do. As a result, the hybrid model outperformed its classical counterpart and achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions.