arrow
Return

Energy diffusion in weakly interacting chains with fermionic dissipation assisted operator evolution

delete2024-08-26
delete1
delete
OA
AI
E
En-Jui Kuo *
B
Brayden Ware
P
Peter Lunts
M
Mohammad Hafezi
C
Christopher D. White
DOI:10.1103/PhysRevB.110.075149delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Interacting lattice Hamiltonians at high temperature generically give rise to energy transport governed by the classical diffusion equation; however, predicting the rate of diffusion requires numerical simulation of the microscopic quantum dynamics. For the purpose of predicting such transport properties, computational time evolution methods must be paired with schemes to control the growth of entanglement to tractably simulate for sufficiently long times. One such truncation scheme-dissipation-assisted operator evolution (DAOE)-controls entanglement by damping out components of operators with large Pauli weight. In this paper, we generalize DAOE to treat fermionic systems. Our method instead damps out components of operators with large fermionic weight. We investigate the performance of DAOE, the new fermionic DAOE (FDAOE), and another simulation method, density matrix truncation (DMT), in simulating energy transport in an interacting one-dimensional Majorana chain. The chain is found to have a diffusion coefficient scaling like interaction strength to the fourth power, contrary to naive expectations based on Fermi's Golden rule-but consistent with recent predictions based on the theory of weak integrability breaking. In the weak interaction regime where the fermionic nature of the system is most relevant, FDAOE is found to simulate the system more efficiently than DAOE.

Journal

Physical Review B cover
Physical Review B
IF:
3.7
Papers:
15.4W
Citations:
41.0W

Organization

University System of Maryland cover
University System of Maryland
Scholars:
6.4W
Papers: 5.6W
Citations: 113
N
national institute of standards & technology (nist) - usa
Scholars:
9.7K
Papers: 9.0K
Citations: 4