Tensor-network strong-disorder renormalization groups for random quantum spin systems in two dimensions

Kouichi Seki, Toshiya Hikihara, and Kouichi Okunishi
Phys. Rev. B 102, 144439 – Published 26 October 2020

Abstract

Novel randomness-induced disordered ground states in two-dimensional (2D) quantum spin systems have been attracting much interest. For quantitative analysis of such random quantum spin systems, one of the most promising numerical approaches is the tensor-network strong-disorder renormalization group (tSDRG), which was basically established for one-dimensional (1D) systems. In this paper, we propose a possible improvement of its algorithm toward 2D random spin systems, focusing on a generating process of the tree network structure of tensors, and precisely examine their performances for the random antiferromagnetic Heisenberg model not only on the 1D chain but also on the square- and triangular-lattices. On the basis of comparison with the exact numerical results up to 36 site systems, we demonstrate that accuracy of the optimal tSDRG algorithm is significantly improved for the 1D and 2D systems in the strong-randomness regime.

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  • Received 23 June 2020
  • Revised 18 September 2020
  • Accepted 21 September 2020

DOI:https://doi.org/10.1103/PhysRevB.102.144439

©2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Kouichi Seki1, Toshiya Hikihara2, and Kouichi Okunishi1

  • 1Department of Physics, Niigata University, Niigata 950-2181, Japan
  • 2Faculty of Science and Technology, Gunma University, Kiryu, Gunma 376-8515, Japan

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Issue

Vol. 102, Iss. 14 — 1 October 2020

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