论文标题
改进了高阶张量重新归一化组方法的局部截短方案
Improved local truncation schemes for the higher-order tensor renormalization group method
论文作者
论文摘要
高阶张量重新归一化组是一种张量 - 网络方法，可为热平衡中的经典和量子系统的分区函数和热力学观测值提供估计。在迭代阻塞过程的每个步骤中，将粗网格张量截断以保持张量尺寸的控制。对于一致的张量阻塞过程，至关重要的是，向前和向后张量模式投影在相同的较低维子空间上。在本文中，我们介绍了两种方法，即SUPERQ和迭代SuperQ方法，用于构造张量截断，以减少甚至最小化局部近似误差，同时满足此约束。
The higher-order tensor renormalization group is a tensor-network method providing estimates for the partition function and thermodynamical observables of classical and quantum systems in thermal equilibrium. At every step of the iterative blocking procedure, the coarse-grid tensor is truncated to keep the tensor dimension under control. For a consistent tensor blocking procedure, it is crucial that the forward and backward tensor modes are projected on the same lower dimensional subspaces. In this paper we present two methods, the SuperQ and the iterative SuperQ method, to construct tensor truncations that reduce or even minimize the local approximation errors, while satisfying this constraint.
