论文标题
简单的启发式方法，用于有效平行张量收缩和量子电路模拟
Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation
论文作者
论文摘要
张量网络是各种计算科学中的主要构件，范围从多体理论和量子计算到概率和机器学习。在这里，我们提出了一种使用概率图形模型来收缩张量网络的平行算法。我们的方法基于图理论中$ $ $ - 树的删除问题的启发式解决方案。我们将所得算法应用于随机量子电路的模拟，并讨论一般张量网络收缩的扩展。
Tensor networks are the main building blocks in a wide variety of computational sciences, ranging from many-body theory and quantum computing to probability and machine learning. Here we propose a parallel algorithm for the contraction of tensor networks using probabilistic graphical models. Our approach is based on the heuristic solution of the $μ$-treewidth deletion problem in graph theory. We apply the resulting algorithm to the simulation of random quantum circuits and discuss the extensions for general tensor network contractions.
