论文标题
哈伯德模型的随机和张量网络模拟
Stochastic and Tensor Network simulations of the Hubbard Model
论文作者
论文摘要
哈伯德模型是了解各种材料的电气性能的重要工具。更具体地说，在蜂窝晶格上，它用于描述石墨烯，以预测从半学到莫特绝缘状态的量子相变。在这项工作中，提出了两种不同的数值技术，这些技术已用于模拟哈伯德模型：一方面的混合蒙特卡洛算法使我们能够模拟前所未有的大型晶格，而张量网络可以用来完全避免符号问题。讨论了这些方法的各自优势和劣势。
The Hubbard model is an important tool to understand the electrical properties of various materials. More specifically, on the honeycomb lattice it is used to describe graphene predicting a quantum phase transition from a semimetal to a Mott insulating state. In this work two different numerical techniques are presented that have been employed for simulations of the Hubbard model: The Hybrid Monte Carlo algorithm on the one hand allowed us to simulate unprecedentedly large lattices, whereas Tensor Networks can be used to completely avoid the sign problem. Respective strengths and weaknesses of the methods are discussed.
