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Quantum-HPC hybrid computation of biomolecular excited-state energies

Kentaro Yamamoto, Riku Masui, Takahito Nakajima, Miwako Tsuji, Mitsuhisa Sato, Peter Schow, Lukas Heidemann, Matthew Burke, Philipp Seitz, Oliver J. Backhouse, Juan W. Pedersen, John Children, Craig Holliman, Nathan Lysne, Daichi Okuno, Seyon Sivarajah, David Muñoz Ramo, Alex Chernoguzov, Ross Duncan

TL;DR

A workflow within the ONIOM framework is developed and demonstrated on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer, enabling scalable and accurate simulation of complex biomolecular reactions.

Abstract

We develop a workflow within the ONIOM framework and demonstrate it on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer. This hybrid platform extends the layered approach for biomolecular chemical reactions to accurately treat the active site, such as a protein, and the large and often weakly correlated molecular environment. Our result marks a significant milestone in enabling scalable and accurate simulation of complex biomolecular reactions

Quantum-HPC hybrid computation of biomolecular excited-state energies

TL;DR

A workflow within the ONIOM framework is developed and demonstrated on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer, enabling scalable and accurate simulation of complex biomolecular reactions.

Abstract

We develop a workflow within the ONIOM framework and demonstrate it on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer. This hybrid platform extends the layered approach for biomolecular chemical reactions to accurately treat the active site, such as a protein, and the large and often weakly correlated molecular environment. Our result marks a significant milestone in enabling scalable and accurate simulation of complex biomolecular reactions
Paper Structure (1 section, 6 equations, 4 figures, 1 table)

This paper contains 1 section, 6 equations, 4 figures, 1 table.

Table of Contents

  1. Histogram of the measurement outcomes

Figures (4)

  • Figure 1: (a) Schematic representation of the biomolecular system consisting of the active site and the environment. (b) Typical scheme of the workflow. The active site is treated with quantum computing (QC), while everything else, including the computationally intensive environment, is handled on HPC. This layered approach enables efficient, accurate simulations of complex biomolecular reactions.
  • Figure 2: (a) Model system in vacume and (b) model system embedded CNT, both in the TS* state for biomolecular excited states involving conical intersections. The retinal molecule serves as the active site, while the CNT provides the environmental effect. (c) Potential energies relative to the energy of 11-cis for each model system. We connect the ground-state energies to facilitate comparison.
  • Figure 3: Histogram of the measurement outcomes $c$ (configurations) for the S$_1$ state of retinal embedded in a carbon nanotube. The horizontal axis represents the configuration indices, while the vertical axis indicates the probability $p$ of each configuration being measured. The red bars represent the configurations to be identified by TE-QSCI, while the gray bars indicate those identified in the classical baseline calculation. (a) exact CASCI and (b)-(e) TE-QSCI results from Reimei with different time step sizes.
  • Figure A 4: Histogram of the measurement outcomes $c$ (configurations) for (A) S$_0$ and (B) $T_{0}$ states. For each panel, (a) exact CASCI and (b)-(e) TE-QSCI results from Reimei with different time step sizes.