Catalytically efficient enzymes bind substrates with high affinity, stabilize the transition state and swiftly release reaction products. The active site must be complementary to the transition state, further stabilized by an electrostatically pre-organized protein environment and hydrogen bonds. Catalytic residues are often highly rigid. Access tunnels connecting the active site cavities with the protein surface, which transfers ligands and waters, are frequently lined by flexible loops. These loops undergo intrinsic or ligand-induced conformational changes, assisting the entry of substrates and releasing products after the reaction. Both physical and chemical steps can be limiting. Optimization of the active site geometry can be achieved by quantum mechanics or diffusion models like RF Diffusion . The geometry of tunnels can be studied by programs like Caver 41 , while ligand transport can be simulated by accelerated molecular dynamics or approximated by methods like CaverDock 42 . The clustering of tunnels in multiple snapshots is demanding, hitting the limits of current classical computers, and quantum clustering holds the potential to break those limits . Moreover, current machine-learning models work for static structures, while predicting dynamic structures is an unsolved problem, partially due to lacking training data. Quantum computers will provide access to highly standardized molecular dynamics simulations to train machine-learning models. Moreover, long simulations will allow the study of ligand transport in real time to design mutations far from the active site. Multiple quantum simulations 45 may help us rationalize and resolve possible substrate and product inhibitions, which are rarely discussed, but critical for the practical use of engineered biocatalysts.