In recent years, machine learning (ML) has emerged as a powerful tool in chemistry, enabling the discovery of new patterns in data, providing insights beyond simple models, accelerating computations, and facilitating the exploration of chemical space. For computational chemists, a primary motivation for applying ML is to bypass the explicit calculation of molecular properties, which can be computationally prohibitive for large data sets. ML has been successfully applied to a wide range of problems, including accelerating molecular simulations, predicting molecular properties, and discovering new catalysts, drugs, and materials. More recently, quantum machine learning (QML) has attracted significant interest due to its potential to offer quantum speedups for certain applications across a variety of implementations. In this talk, we present an overview of our QML research within the Quantum Software Consortium and, if time permits, highlight recent advances in distributed quantum computing (DQC) within the quantum-centric supercomputing (QCSC) framework for quantum chemical applications.