Leveraging analog quantum computing with neutral atoms for solvent configuration prediction in drug discovery

Abstract

We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design.