Entanglement-based tensor-network strong-disorder renormalization group

TL;DR

This paper introduces an entanglement-based tensor-network strong-disorder renormalization group algorithm for quantum spin systems, improving accuracy in some cases by utilizing entanglement structure instead of energy spectra.

Contribution

The paper presents a novel entanglement-based tSDRG algorithm that directly uses entanglement structure for renormalization, differing from previous energy spectrum-based methods.

Findings

Better accuracy for square lattice with weak randomness

Less efficient for 1D and triangular lattices in strong randomness

Discusses theoretical background and potential improvements

Abstract

We propose an entanglement-based algorithm of the tensor-network strong-disorder renormalization group (tSDRG) method for quantum spin systems with quenched randomness. In contrast to the previous tSDRG algorithm based on the energy spectrum of renormalized block Hamiltonians, we directly utilizes the entanglement structure associated with the blocks to be renormalized. We examine accuracy of the new algorithm for the random antiferromagnetic Heisenberg models on the one-dimensional, triangular, and square lattices. We then find that the entanglement-based tSDRG achieves better accuracy than the previous one for the square lattice model with weak randomness, while it is less efficient for the one-dimensional and triangular lattice models particularly in the strong randomness region. The theoretical background and possible improvements of the algorithm are also discussed.