Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 73 tok/s
Gemini 2.5 Pro 51 tok/s Pro
GPT-5 Medium 31 tok/s Pro
GPT-5 High 32 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 218 tok/s Pro
GPT OSS 120B 460 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Numerical Assessment for Accuracy and GPU Acceleration of TD-DMRG Time Evolution Schemes (1907.12044v2)

Published 28 Jul 2019 in physics.chem-ph and physics.comp-ph

Abstract: Time dependent density matrix renormalization group (TD-DMRG) has become one of the cutting edge methods of quantum dynamics for complex systems. In this paper, we comparatively study the accuracy of three time evolution schemes in TD-DMRG, the global propagation and compression method with Runge-Kutta algorithm (P&C-RK), the time dependent variational principle based methods with matrix unfolding algorithm (TDVPMU) and with projector-splitting algorithm (TDVP-PS), by performing benchmarks on the exciton dynamics of Fenna-Matthews-Olson (FMO) complex. We show that TDVP-MU and TDVP-PS yield the same result when the time step size is converged and they are more accurate than P&C-RK4, while TDVP-PS tolerates a larger time step size than TDVP-MU. We further adopt the graphical processing units (GPU) to accelerate the heavy tensor contractions in TD-DMRG and it is able to speed up the TDVP-MU and TDVP-PS schemes by up to 73 times.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.