Dynamical simulations of classical stochastic systems using matrix product states

TL;DR

This paper adapts the TEBD algorithm to simulate the dynamics of classical stochastic systems using matrix product states, enabling efficient and accurate modeling of non-equilibrium processes like TASEP.

Contribution

It introduces a method combining TEBD with MPS for classical stochastic systems, comparing NMF and SVD factorizations, and demonstrates high accuracy in simulating TASEP.

Findings

SVD provides more accurate approximations than NMF.

TEBD accurately reproduces TASEP steady state with small MPS dimension.

Expectation values converge with increasing MPS dimension.

Abstract

We adapt the time-evolving block decimation (TEBD) algorithm, originally devised to simulate the dynamics of 1D quantum systems, to simulate the time-evolution of non-equilibrium stochastic systems. We describe this method in detail; a system's probability distribution is represented by a matrix product state (MPS) of finite dimension and then its time-evolution is efficiently simulated by repeatedly updating and approximately re-factorizing this representation. We examine the use of MPS as an approximation method, looking at parallels between the interpretations of applying it to quantum state vectors and probability distributions. In the context of stochastic systems we consider two types of factorization for use in the TEBD algorithm: non-negative matrix factorization (NMF), which ensures that the approximate probability distribution is manifestly non-negative, and the singular value decomposition (SVD). Comparing these factorizations we find the accuracy of the SVD to be substantially greater than current NMF algorithms. We then apply TEBD to simulate the totally asymmetric simple exclusion process (TASEP) for systems of up to hundreds of lattice sites in size. Using exact analytic results for the TASEP steady state, we find that TEBD reproduces this state such that the error in calculating expectation values can be made negligible, even when severely compressing the description of the system by restricting the dimension of the MPS to be very small. Out of the steady state we show for specific observables that expectation values converge as the dimension of the MPS is increased to a moderate size.