Finite time large deviations via matrix product states

Abstract

Recent work has shown the effectiveness of tensor network methods for computing large deviation functions in constrained stochastic models in the infinite time limit. Here we show that these methods can also be used to study the statistics of dynamical observables at arbitrary finite time. This is a harder problem because, in contrast to the infinite time case where only the extremal eigenstate of a tilted Markov generator is relevant, for finite time the whole spectrum plays a role. We show that finite time dynamical partition sums can be computed efficiently and accurately in one dimension using matrix product states, and describe how to use such results to generate rare event trajectories on demand. We apply our methods to the Fredrickson-Andersen (FA) and East kinetically constrained models, and to the symmetric simple exclusion process (SSEP), unveiling dynamical phase diagrams in terms of counting field and trajectory time. We also discuss extensions of this method to higher dimensions.

6+2 pages, 3+2 figures

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Cited by in corpus (8)

• Optimal sampling of dynamical large deviations in two dimensions via tensor networks
• Slow dynamics and large deviations in classical stochastic Fredkin chains
• Graph-combinatorial approach for large deviations of Markov chains
• Using tensor network states for multi-particle Brownian ratchets
• Quantifying Rare Events in Stochastic Reaction-Diffusion Dynamics Using Tensor Networks
• Delocalization-localization dynamical phase transition of random walks on graphs
• Tensor-Network Approaches to Counting Statistics for the Current in a Boundary-Driven Diffusive System
• Anomalous fluctuations of renewal-reward processes with heavy-tailed distributions