Tensor-network strong-disorder renormalization groups for random quantum spin systems in two dimensions
Year: 2020
Type: article
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.
Institutions: Niigata University, Gunma University
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: Study of quantum many-body systems with frustration and randomness using tensor-network algorithm and quantum information, Energy-scale controlling of quantum many-body systems and entanglemet renormalization, Exploration of novel quantum many-body states induced by frustration and randomness
OpenAlex ID: W3036582133