论文标题
纠缠两分和树张量网络
Entanglement bipartitioning and tree tensor networks
论文作者
论文摘要
我们提出了纠缠两部分方法，以设计量子多体系统的树刺网 - 网络（TTN）的最佳网络结构。给定精确的地面波函数，我们对自旋簇节点进行顺序两部分，以最大程度地减少互信息或与要分配的分支相关的纠缠熵的最大损失。我们证明，多达16个站点的纠缠两部分在一个和二维中，$ s = 1/2 $ heisenberg模型的非平凡树网络结构都会增加。与标准TTN（例如均匀矩阵乘积状态和完美的二元树张量网络）相比，所得的TTN使我们能够获得更好的变分能。
We propose the entanglement bipartitioning approach to design an optimal network structure of the tree-tensor-network (TTN) for quantum many-body systems. Given an exact ground-state wavefunction, we perform sequential bipartitioning of spin-cluster nodes so as to minimize the mutual information or the maximum loss of the entanglement entropy associated with the branch to be bipartitioned. We demonstrate that entanglement bipartitioning of up to 16 sites gives rise to nontrivial tree network structures for $S=1/2$ Heisenberg models in one and two dimensions. The resulting TTNs enable us to obtain better variational energies, compared with standard TTNs such as uniform matrix product state and perfect-binary-tree tensor network.
