English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce quantum algorithms able to sample equilibrium water solventmolecules configurations within proteins thanks to analog quantum computing. Todo so, we combine a quantum placement strategy to the 3D Reference InteractionSite Model (3D-RISM), an approach capable of predicting continuous solventdistributions. The intrinsic quantum nature of such coupling guaranteesmolecules not to be placed too close to each other, a constraint usuallyimposed by hand in classical approaches. We present first a full quantumadiabatic evolution model that uses a local Rydberg Hamiltonian to cast thegeneral problem into an anti-ferromagnetic Ising model. Its solution, anNP-hard problem in classical computing, is embodied into a Rydberg atom arrayQuantum Processing Unit (QPU). Following a classical emulator implementation, aQPU portage allows to experimentally validate the algorithm performances on anactual quantum computer. As a perspective of use on next generation devices, weemulate a second hybrid quantum-classical version of the algorithm. Such avariational quantum approach (VQA) uses a classical Bayesian minimizationroutine to find the optimal laser parameters. Overall, these Quantum-3D-RISM(Q-3D-RISM) algorithms open a new route towards the application of analogquantum computing in molecular modelling and drug design.

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery