1 research outputs found
Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery
We introduce quantum algorithms able to sample equilibrium water solvent
molecules configurations within proteins thanks to analog quantum computing. To
do so, we combine a quantum placement strategy to the 3D Reference Interaction
Site Model (3D-RISM), 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 anti-ferromagnetic Ising model. Its solution, an
NP-hard problem in classical computing, is embodied into a Rydberg atom array
Quantum Processing Unit (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 (VQA) uses a classical Bayesian minimization
routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM
(Q-3D-RISM) algorithms open a new route towards the application of analog
quantum computing in molecular modelling and drug design