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arXiv · 2308.04291

Converting long-range entanglement into mixture: tensor-network approach to local equilibration

Miguel Frías-Pérez·Luca Tagliacozzo·Mari Carmen Bañuls
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Abstract

In the out-of-equilibrium evolution induced by a quench, fast degrees of freedom generate long-range entanglement that is hard to encode with standard tensor networks. However, local observables only sense such long-range correlations through their contribution to the reduced local state as a mixture. We present a tensor network method that identifies such long-range entanglement and efficiently transforms it into mixture, much easier to represent. In this way, we obtain an effective description of the time-evolved state as a density matrix that captures the long-time behavior of local operators with finite computational resources.

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Miguel Frías-Pérez, Luca Tagliacozzo, Mari Carmen Bañuls. 2023-08-08. Converting long-range entanglement into mixture: tensor-network approach to local equilibration. https://doi.org/10.1103/physrevlett.132.100402

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