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

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