Robust Quantum Reservoir Learning for Molecular Property Prediction

Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing has led to the extensive exploration of quantum machine learning algorithms. Quantum variational machine learning algorithms are currently the most prevalent, but they face issues with trainability due to vanishing gradients. An emerging alternative is the quantum reservoir computing (QRC) approach, in which the quantum algorithm does not require gradient evaluation on the quantum hardware. Motivated by the potential advantages of the QRC method, we apply it to predict the biological activity of potential drug molecules based on molecular descriptors. We observe more robust QRC performance as the size of the data set decreases, compared to standard classical models, a quality of potential interest for pharmaceutical data sets of limited size. In addition, we leverage the uniform manifold approximation and projection technique to analyze structural changes as classical features are transformed through quantum dynamics, and we find that quantum reservoir embeddings provide better separability between active and inactive compounds in the low-dimensional learned feature space.