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arXiv · 1710.01463

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states

Lucas Kohn·Ferdinand Tschirsich·Maximilian Keck·Martin B. Plenio·Dario Tamascelli·Simone Montangero
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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 TEBD and DMRG-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond- or local dimension, of up to 24 times in quasi-2D cylindrical systems.

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Lucas Kohn, Ferdinand Tschirsich, Maximilian Keck, Martin B. Plenio, Dario Tamascelli, Simone Montangero. 2017-10-04. Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states. https://doi.org/10.1103/physreve.97.013301

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