Few-body systems capture many-body physics: Tensor network approach

Few-body systems capture many-body physics: Tensor network approach
Shi-Ju Ran,1,* Angelo Piga,1 Cheng Peng,2 Gang Su,2,3 and Maciej Lewenstein1,4
1ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
2Theoretical Condensed Matter Physics and Computational Materials Physics Laboratory, School of Physical Sciences,
University of Chinese Academy of Sciences, Beijing 100049, China
3Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China
4ICREA, Passeig Lluis Companys 23, 08010 Barcelona, Spain
(Received 10 April 2017; revised manuscript received 29 September 2017; published 13 October 2017)

Due to the presence of strong correlations, theoretical or experimental investigations of quantum many-body systems belong to the most challenging tasks in modern physics. Stimulated by tensor networks, we propose a scheme of constructing the few-body models that can be easily accessed by theoretical or experimental means, to accurately capture the ground-state properties of infinite many-body systems in higher dimensions. The general idea is to embed a small bulk of the infinite model in an “entanglement bath” so that the many-body effects can be faithfully mimicked. The approach we propose is efficient, simple, flexible, sign-problem free, and it directly accesses the thermodynamic limit. The numerical results of the spin models on honeycomb and simple cubic lattices show that the ground-state properties including quantum phase transitions and the critical behaviors are accurately captured by only O(10) physical and bath sites. Moreover, since the few-body Hamiltonian only contains local interactions among a handful of sites, our work provides different ways of studying the many-body phenomena in the infinite strongly correlated systems by mimicking them in the few-body experiments using cold atoms/ions, or developing quantum devices by utilizing the many-body features.