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

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 even for the 2D systems in the strong-randomness regime.

arXiv comment10 pages, 11 figures

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Cited by in corpus (8)

• Developments in the Tensor Network -- from Statistical Mechanics to Quantum Entanglement
• Automatic structural optimization of tree tensor networks
• Adaptive-weighted tree tensor networks for disordered quantum many-body systems
• Entanglement bipartitioning and tree tensor networks
• Improving accuracy of tree-tensor network approach by optimization of network structure
• Entanglement-based tensor-network strong-disorder renormalization group
• TTNOpt: Tree tensor network package for high-rank tensor compression
• Hierarchical Search of Tree Tensor Networks for High-Dimensional Data