Sampling rare conformational transitions with a quantum computer

Year: 2022

Authors: Ghamari D., Hauke P., Covino R., Faccioli P.

Autors Affiliation: Univ Trento, Dept Phys, Via Sommar 14, I-38123 Trento, Italy; INFN TIFPA, Via Sommar 14, I-38123 Trento, Italy; Univ Trento, INO CNR BEC Ctr, Via Sommar 14, I-38123 Trento, Italy; Frankfurt Inst Adv Studies, Ruth Moufang Str 1, D-60438 Frankfurt, Germany.

Abstract: Structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, Molecular Dynamics (MD) simulations enable us to investigate these thermally activated processes with an atomic level of resolution. In practice, an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods focus the computational power on sampling the rare transitions between states. One of their outstanding limitations is to efficiently generate paths that visit significantly different regions of the conformational space. To overcome this issue, we introduce a new algorithm for MD simulations that integrates machine learning and quantum computing. First, using functional integral methods, we derive a rigorous low-resolution spatially coarse-grained representation of the system’s dynamics, based on a small set of molecular configurations explored with machine learning. Then, we use a quantum annealer to sample the transition paths of this low-resolution theory. We provide a proof-of-concept application by simulating a benchmark conformational transition with all-atom resolution on the D-Wave quantum computer. By exploiting the unique features of quantum annealing, we generate uncorrelated trajectories at every iteration, thus addressing one of the challenges of path sampling. Once larger quantum machines will be available, the interplay between quantum and classical resources may emerge as a new paradigm of high-performance scientific computing. In this work, we provide a platform to implement this integrated scheme in the field of molecular simulations.

Journal/Review: SCIENTIFIC REPORTS

Volume: 12 (1)      Pages from: 16336-1  to: 16336-11

More Information: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 804305), State of Hesse (Grant Landes-Offensive zur Entwicklung Wissenschaftlich-Okonomischer Exzellenz LOEWE CMMS), CINECA (Grant ISCRA-C).
KeyWords: Free-energy; Simulation
DOI: 10.1038/s41598-022-20032-x

ImpactFactor: 4.600
Citations: 6
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-03
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