From Quantum Computing Algorithms to Human Health Applications: Tunnelling through the Disciplinary Barriers

We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome Leap as part of their Quantum for Bio Program. We aim to use quantum computing to guide the discovery of covalent inhibitors for the treatment of the rare disease myotonic dystrophy type I. Modelling the formation of a chemical bond in the context of the rest of the inhibitor and the protein is a challenge, because of the size of the systems of interest, the intricacies of the electronic structure where the importance of electron correlation may vary between reactant and transition states, and the extensive sampling of configurational space that is often required. We are developing an efficient hybrid quantum-classical framework to address the accuracy and scalability limitations of conventional computational drug design approaches. A quantum-enhanced density functional approximation is developed based on data from computation on neutral-atom quantum hardware, which can then be deployed within conventional quantum mechanics/molecular mechanics calculations. Our quantum-enhanced framework can be systematically updated as more capable quantum processors become available, heralding the prospect of using quantum-derived data to train improved density functional approximations and, ultimately, to support a wide range of drug design efforts.