Arceci L, Silvi P, Montangero S
Dipartimento di Fisica e Astronomia "G. Galilei," Università di Padova, I-35131 Padova, Italy.
INFN, Sezione di Padova, I-35131 Padova, Italy.
Phys Rev Lett. 2022 Jan 28;128(4):040501. doi: 10.1103/PhysRevLett.128.040501.
We present a numerical strategy to efficiently estimate bipartite entanglement measures, and in particular the entanglement of formation, for many-body quantum systems on a lattice. Our approach exploits the tree tensor operator tensor network Ansatz, a positive loopless representation for density matrices which, as we demonstrate, efficiently encodes information on bipartite entanglement, enabling the upscaling of entanglement estimation. Employing this technique, we observe a finite-size scaling law for the entanglement of formation in 1D critical lattice models at finite temperature for up to 128 spins, extending to mixed states the scaling law for the entanglement entropy.
我们提出了一种数值策略，用于有效估计晶格上多体量子系统的二分纠缠度量，特别是形成纠缠。我们的方法利用树张量算子张量网络假设，这是一种密度矩阵的正无环表示，正如我们所证明的，它能有效编码二分纠缠信息，从而实现纠缠估计的规模扩展。采用这种技术，我们观察到在有限温度下，一维临界晶格模型中形成纠缠的有限尺寸标度律，对于多达128个自旋的情况都适用，将纠缠熵的标度律扩展到了混合态。
