Source-linked AI summary
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
Biomolecular reactions are difficult to simulate because active sites interact with large environments, and strong electron correlation challenges conventional approaches. This work combines TE-QSCI with an ONIOM-based quantum-HPC workflow, demonstrating improved excitation energies and state fidelity over the initial classical baseline. The results show that TE-QSCI can identify additional configurations beyond the initial active space, while circuit complexity remains a scaling concern.
Problem
Biomolecular reaction simulation must account for complex active-site and environment interactions, while strong electron correlation is challenging for conventional approximate QM methods.
Source: p. 1Method
The workflow combines ONIOM layering with TE-QSCI to identify configurations outside an initial active space, using quantum computing for the active site and conventional methods for the environment.
Source: pp. 2–3Results
TE-QSCI identified the first 6 configurations in CASCI(8,8) with high probability and improved excitation energy and state fidelity over the initial classical baseline.
Source: p. 4Takeaways & Limitations
The demonstrated Fugaku–Reimei workflow extends TE-QSCI to identify configurations beyond the active space and can be adapted to other layered approaches such as QM/MM.
Source: p. 4The time-evolution circuit scales as O(N^4), and the current workflow is unlikely to scale beyond the limits of brute-force HPC simulation at about 50 qubits without circuit-size reduction.
Source: p. 4
Abstract
from arXiv · showhide
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
Appendix A: Histogram of the measurement outcomes
Appendix A compares measurement-outcome histograms for S0 and T0, showing that T0 behaves like S1 while S0 yields fewer new configurations.
Appendix A: Histogram of the measurement outcomes
- S0 yields a modest number of new configurations, whereas T0 shows behavior similar to S1.The S0 initial state has significantly higher fidelity.Source: Appendix A · p. 7
- The histograms show measurement outcomes c, representing configurations, for S0 and T0.Source: Figure A4 · p. 7
- Each state panel compares exact CASCI outcomes with Reimei TE-QSCI results at different time-step sizes.Source: Figure A4 · p. 7