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This paper is about classical tensor-network methods for simulating how quenched quantum many-body systems thermalize. It proposes converting hard-to-represent long-range entanglement into a mixed-state description that still captures local observables over long times. The work matters to quantum readers mainly as a classical simulation technique for quantum dynamics, which is relevant to benchmarking and validating quantum simulators, not as a demonstration of quantum hardware.
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Converting long-range entanglement into mixture: tensor-network approach to local equilibration

arXiv2 min read

arXiv:2308.04291v2 Announce Type: replace 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.

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.

5 pages, 4 figures. Published version; Supplemental Material and ancillary data added

Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Strongly Correlated Electrons (cond-mat.str-el)

arXiv:2308.04291 [quant-ph]

(or arXiv:2308.04291v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2308.04291 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. Lett. 132, 100402 (2024)

https://doi.org/10.1103/PhysRevLett.132.100402 Focus to learn more DOI(s) linking to related resources

entropyDecayIntegrable/res_entropy_J1_1.0_J2_0.0_g_0.5_h_0.0.npy

entropyDecayIntegrable/res_entropy_J1_1.0_J2_0.0_g_1.0_h_0.0.npy

entropyDecayIntegrable/res_entropy_J1_1.0_J2_0.0_g_1.5_h_0.0.npy

entropyDecayIntegrable/res_entropy_J1_1.0_J2_0.0_g_2.0_h_0.0.npy

freeFermions/entanglementOptimization.py

freeFermions/initialStates/occupation_number.py

freeFermions/tools.py

truncationIntegrable/res_simple_trick_g2.0.npy

truncationIntegrable/res_tol_entropy0_g2_.npy

truncationIntegrable/res_tol_entropy10_g2_.npy

truncationIntegrable/res_tol_entropy15_g2_.npy

truncationIntegrable/res_tol_entropy20_g2_.npy

truncationIntegrable/res_tol_entropy25_g2_.npy

truncationIntegrable/res_tol_entropy30_g2_.npy

truncationIntegrable/res_tol_entropy5_g2_.npy

truncationNonIntegrable/Results/res_tol_entropy_0_J2_0.1_.npy

truncationNonIntegrable/Results/res_tol_entropy_20_J2_0.1_.npy

truncationNonIntegrable/Results/res_tol_entropy_25_J2_0.1_.npy

truncationNonIntegrable/Results/res_tol_entropy_30_J2_0.1_.npy

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Full text under the publisher's open licence · 252 words

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