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arXiv · 2201.03531

Using tensor network states for multi-particle Brownian ratchets

Nils E. Strand·Hadrien Vroylandt·Todd R. Gingrich
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Abstract

The study of Brownian ratchets has taught how time-periodic driving supports a time-periodic steady state that generates nonequilibrium transport. When a single particle is transported in one dimension, it is possible to rationalize the current in terms of the potential, but experimental efforts have ventured beyond that single-body case to systems with many interacting carriers. Working with a lattice model of volume-excluding particles in one dimension, we analyze the impact of interactions on a flashing ratchet's current. To surmount the many-body problem, we employ the time-dependent variational principle with a binary tree tensor network, methods discussed at length in a companion paper. Rather than propagating individual trajectories, the tensor network approach propagates a distribution over many-body configurations via a controllable variational approximation. The calculations, which reproduce Gillespie trajectory sampling, identify and explain a shift in the frequency of maximum current to higher driving frequency as the lattice occupancy increases.

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Nils E. Strand, Hadrien Vroylandt, Todd R. Gingrich. 2022-01-10. Using tensor network states for multi-particle Brownian ratchets. https://doi.org/10.1063/5.0097332

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