2018
DOI: 10.1103/physreve.97.013301
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Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states

Abstract: We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets cl… Show more

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Cited by 14 publications
(15 citation statements)
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References 79 publications
(100 reference statements)
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“…where the angular frequency is defined in Eq. (25). In particular, P 1 (0, t|λ) is periodic with period T N := π/ω N (λ), it is symmetric with respect to λ * = −1/N, and P 1 (0, t|λ * ) = 0, which means that the walker occupies only the outer vertices of the star graph.…”
Section: Star Graphmentioning
confidence: 99%
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“…where the angular frequency is defined in Eq. (25). In particular, P 1 (0, t|λ) is periodic with period T N := π/ω N (λ), it is symmetric with respect to λ * = −1/N, and P 1 (0, t|λ * ) = 0, which means that the walker occupies only the outer vertices of the star graph.…”
Section: Star Graphmentioning
confidence: 99%
“…with ω N (λ) defined in Eq. (25). Due to the symmetry of the graph, both the QFI and the FI do not depend on the starting vertex, i.e., the estimation is completely indifferent to the choice of the initially localized state.…”
Section: Complete Graphmentioning
confidence: 99%
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“…For the examples provided in the main text, chains of n=15 sites are enough to avoid boundary effects. In order to further optimize our simulations, we augmented our TEDOPA code with a reduced-rank randomized singular value decomposition (RRSVD) routine [57,58]. Singular value decomposition (SVD) is at the heart of the dimensionality reduction TEBD relies on.…”
Section: Appendix B Tdmrg Simulations Using the Tedopa Algorithmmentioning
confidence: 99%