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Probing Off-diagonal Eigenstate Thermalization with Tensor Networks
- Source :
- Phys. Rev. B 109, 134304 (2024)
- Publication Year :
- 2023
-
Abstract
- Energy filter methods in combination with quantum simulation can efficiently access the properties of quantum many-body systems at finite energy densities [Lu et al. PRX Quantum 2, 020321 (2021)]. Classically simulating this algorithm with tensor networks can be used to investigate the microcanonical properties of large spin chains, as recently shown in [Yang et al. Phys. Rev. B 106, 024307 (2022)]. Here we extend this strategy to explore the properties of off-diagonal matrix elements of observables in the energy eigenbasis, fundamentally connected to the thermalization behavior and the eigenstate thermalization hypothesis. We test the method on integrable and non-integrable spin chains of up to 60 sites, much larger than accessible with exact diagonalization. Our results allow us to explore the scaling of the off-diagonal functions with the size and energy difference, and to establish quantitative differences between integrable and non-integrable cases.<br />Comment: Accepted version, 16 pages, 8 figures
- Subjects :
- Quantum Physics
Subjects
Details
- Database :
- arXiv
- Journal :
- Phys. Rev. B 109, 134304 (2024)
- Publication Type :
- Report
- Accession number :
- edsarx.2312.00736
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1103/PhysRevB.109.134304