Converting Long-Range Entanglement into Mixture : Tensor-Network Approach to Local Equilibration
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:132 |
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| Enthalten in: |
Physical review letters - 132(2024), 10 vom: 08. März, Seite 100402 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Frías-Pérez, Miguel [VerfasserIn] |
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Date Revised 22.03.2024 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1103/PhysRevLett.132.100402 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370079051 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Bañuls, Mari Carmen |e verfasserin |4 aut | |
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