Tensor-network strong-disorder renormalization groups for random quantum spin systems in two dimensions
| Autor: | Kouichi Okunishi, Kouichi Seki, Toshiya Hikihara |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
Strongly Correlated Electrons (cond-mat.str-el) Statistical Mechanics (cond-mat.stat-mech) Basis (linear algebra) Heisenberg model FOS: Physical sciences Disordered Systems and Neural Networks (cond-mat.dis-nn) 02 engineering and technology Condensed Matter - Disordered Systems and Neural Networks Renormalization group 021001 nanoscience & nanotechnology 01 natural sciences Square (algebra) Renormalization Condensed Matter - Strongly Correlated Electrons 0103 physical sciences Tree network Statistical physics Tensor 010306 general physics 0210 nano-technology Spin (physics) Condensed Matter - Statistical Mechanics |
| Zdroj: | Physical Review B. 102 |
| ISSN: | 2469-9969 2469-9950 |
| DOI: | 10.1103/physrevb.102.144439 |
| Popis: | 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 even for the 2D systems in the strong-randomness regime. Comment: 10 pages, 11 figures |
| Databáze: | OpenAIRE |
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