Tensornetwork approaches to counting statistics for the current in a boundarydriven diffusive system
Abstract
We apply tensor networks to counting statistics for the stochastic particle transport in an outofequilibrium diffusive system. This system is composed of a onedimensional channel in contact with two particle reservoirs at the ends. Two tensornetwork algorithms, namely, density matrix renormalization group and time evolving block decimation, are respectively implemented. The cumulant generating function for the current is numerically calculated and then compared with the analytical solution. Excellent agreement is found, manifesting the validity of these approaches in such an application. Moreover, the fluctuation theorem for the current is shown to hold.
 Publication:

New Journal of Physics
 Pub Date:
 November 2022
 DOI:
 10.1088/13672630/ac9ed7
 arXiv:
 arXiv:2206.05322
 Bibcode:
 2022NJPh...24k3022G
 Keywords:

 tensor networks;
 counting statistics;
 stochastic process;
 fluctuation theorem;
 Condensed Matter  Statistical Mechanics
 EPrint:
 18 pages, 10 figures