Molecular design with automated quantum computing-based deep learning and optimization

Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing(QC)owing to its notable advances in optimization and machine learning.Here,we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks.The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules,while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model.We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements.The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design,thus accentuating its utility.