S. Akiyama, Y. Kuramashi, Takumi Yamashita, Yusuke Yoshimura
{"title":"Phase transition of four-dimensional Ising model with tensor network scheme","authors":"S. Akiyama, Y. Kuramashi, Takumi Yamashita, Yusuke Yoshimura","doi":"10.22323/1.363.0138","DOIUrl":null,"url":null,"abstract":"We investigate the phase transition of the four-dimensional Ising model with two types of tensor network scheme, one is the higher-order tensor renormalization group and the other is the anisotropic tensor renormalization group. The results for the internal energy and magnetization obtained by the former algorithm with the impure tensor method, enlarging the lattice volume up to $1024^4$, are consistent with the weak first-order phase transition. For the later algorithm, our implementation successfully reduces the execution time thanks to the parallel computation and the results provided by ATRG seems comparable to those with HOTRG.","PeriodicalId":8440,"journal":{"name":"arXiv: High Energy Physics - Lattice","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: High Energy Physics - Lattice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22323/1.363.0138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We investigate the phase transition of the four-dimensional Ising model with two types of tensor network scheme, one is the higher-order tensor renormalization group and the other is the anisotropic tensor renormalization group. The results for the internal energy and magnetization obtained by the former algorithm with the impure tensor method, enlarging the lattice volume up to $1024^4$, are consistent with the weak first-order phase transition. For the later algorithm, our implementation successfully reduces the execution time thanks to the parallel computation and the results provided by ATRG seems comparable to those with HOTRG.