aiqbench analysis
This study demonstrates a practical hybrid workflow where a 54-qubit quantum processor acts as an accelerator for correlated electronic-structure calculations within a massive million-atom biomolecular simulation. By integrating CPUs, GPUs, and QPUs, the research successfully applies high-accuracy quantum methods to complex biological systems previously inaccessible to standard classical computing. The work validates the immediate utility of current noisy intermediate-scale quantum devices in scaling scientific simulations beyond classical limits.
Multiscale Quantum-Classical Simulation of Protein Autoproteolysis in a Million-Atom Membrane Environment
arXiv:2610.23108v1 Announce Type: cross Abstract: Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.
Authors:Alexander Orlov, Alexis Ralli, Tim Weaving, Michael Williams de la Bastida, Reuben Martin, Zohim Chandani, Minh Chung, P. V. Sriluckshmy, Fedor Simkovic IV, Luigi Iapichino, Peter J. Love, Christian B. Mendl, Andrea Townsend-Nicholson, Peter V. Coveney
Abstract:Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.
Biomolecules (q-bio.BM); Quantum Physics (quant-ph)
arXiv:2610.23108 [q-bio.BM]
(or arXiv:2610.23108v1 [q-bio.BM] for this version)
https://doi.org/10.48550/arXiv.2610.23108 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 199 words
