Quantum computing could support solutions, for example, molecular dynamics (MD) simulation, which often fails in practice on large problems due to lack of computing power; QM calculations are only accurate to a limited extent; DFT is functionally dependent, and only a rough approximation; real ab initio calculations already fail at benzene with high accuracy. Quantum computing will make large database searches significantly better / more efficient, and molecular modeling -a 3D structure of large molecules (protein folding, conformer calculations) or active side modeling/docking is raised to the next level. The data sets required for machine learning -with quantum machine learning -to train powerful models can be drastically reduced (i.e., less experimental data is needed for the same result, or better predictions can be made with the same amount of data). Automation/robotics benefits from "path optimization" and scheduling calculations (i.e., the same robot has a higher throughput)