Progress Toward Quantum Machine Learning for Chemistry properties in Catalyst Discovery.

We integrate cheminformatics with quantum machine learning (QML) methods to predict chemical properties in catalytic reactions. We evaluate performance metrics for quantum kernel design and demonstrate a use case applying quantum algorithms to chemical property prediction. Comparative analysis with conventional machine learning methods assesses the potential and current limitations of quantum approaches for catalyst discovery.