发表机构
Westlake University（西湖大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出绑腿张量网络态（LETTA），通过共享物理腿编码长程关联，结合MPS骨干，实现超越虚键的纠缠，在二维受挫海森堡和三维横场伊辛模型中显著提高精度。
AI 中文摘要
我们引入一类张量网络态，其中物理腿在局部张量之间共享，称为绑腿张量拟设（LETTA）。物理腿的绑定直接编码长程关联，而虚矩阵乘积态（MPS）骨干保留短程多体纠缠。线性虚骨干使我们能够开发一种确定性的密度矩阵重正化群类变分优化算法，该算法利用活动绑定边界集上的精确收缩和局部最小化。我们展示了LETTA在二维受挫的$J_1$--$J_2$海森堡模型和三维横场伊辛模型上的优势。我们的结果表明，LETTA比相同键维的MPS计算准确得多，并且通常可以达到使用少一个数量级变分参数的更大MPS计算的精度。因此，LETTA为能够编码超越虚键的长程关联的显式关联张量网络态打开了大门。
英文摘要
We introduce a class of tensor-network states in which physical legs are shared among local tensors, termed leg-tied tensor ansätze (LETTA). Physical leg ties encode long-range correlations directly, while a virtual matrix product state (MPS) backbone retains short-range multipartite entanglement. The linear virtual backbone allows us to develop a deterministic density matrix renormalization group-like variational optimization algorithm using exact contractions over the active tie-boundary sets and local minimization. We demonstrate the advantages of LETTA for the two-dimensional frustrated $J_1$--$J_2$ Heisenberg model and the three-dimensional transverse-field Ising model. Our results show that LETTA is substantially more accurate than same-bond-dimension MPS calculations and can typically reach the accuracy of much larger MPS calculations using one order of magnitude fewer variational parameters. LETTA thus opens the door for explicitly correlated tensor-network states that can encode long-range correlation beyond virtual bonds.
Comments14 pages, 5 figures, including Supplemental Material
