Computing time-periodic steady-state currents via the time evolution of tensor network states

Author:

Strand Nils E.1ORCID,Vroylandt Hadrien1ORCID,Gingrich Todd R.1ORCID

Affiliation:

1. Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA

Abstract

We present an approach based upon binary tree tensor network (BTTN) states for computing steady-state current statistics for a many-particle 1D ratchet subject to volume exclusion interactions. The ratcheted particles, which move on a lattice with periodic boundary conditions subject to a time-periodic drive, can be stochastically evolved in time to sample representative trajectories via a Gillespie method. In lieu of generating realizations of trajectories, a BTTN state can variationally approximate a distribution over the vast number of many-body configurations. We apply the density matrix renormalization group algorithm to initialize BTTN states, which are then propagated in time via the time-dependent variational principle (TDVP) algorithm to yield the steady-state behavior, including the effects of both typical and rare trajectories. The application of the methods to ratchet currents is highlighted, but the approach extends naturally to other interacting lattice models with time-dependent driving. Although trajectory sampling is conceptually and computationally simpler, we discuss situations for which the BTTN TDVP strategy can be beneficial.

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

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