Space-local memory in generalized master equations: Reaching the thermodynamic limit for the cost of a small lattice simulation
Srijan Bhattacharyya, Thomas Sayer, Andr\'es Montoya-Castillo

TL;DR
This paper introduces a novel space-time finite memory approach in generalized master equations, enabling efficient simulation of large lattice systems' dynamics without finite-size artifacts, significantly reducing computational costs.
Contribution
The authors develop a new method exploiting finite memory in both space and time to accurately predict large-scale lattice dynamics from small system data.
Findings
Successfully simulated large lattice dynamics in 1D and 2D without finite-size effects.
Reduced computational expense of lattice simulations by multiple orders of magnitude.
Validated approach on nonequilibrium polaron relaxation and transport in the Holstein model.
Abstract
The exact quantum dynamics of lattice models can be computationally intensive, especially when aiming for large system sizes and extended simulation times necessary to converge transport coefficients. By leveraging finite memory times to access long-time dynamics using only short-time data, generalized master equations (GMEs) can offer a route to simulating the dynamics of lattice problems efficiently. However, such simulations are limited to small lattices whose dynamics exhibit finite-size artifacts that contaminate transport coefficient predictions. To address this problem, we introduce a novel approach that exploits finite memory in time \textit{and} space to efficiently predict the many-body dynamics of dissipative lattice problems involving short-range interactions. This advance enables one to leverage the short-time dynamics of small lattices to simulate arbitrarily large systems…
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Taxonomy
TopicsQuantum many-body systems · Neural Networks and Applications · Theoretical and Computational Physics
