论文标题
矩阵产品状态具有回流相关性
Matrix Product States with Backflow correlations
论文作者
论文摘要
通过从相关系统的回流转换中汲取灵感，我们引入了一种新颖的张量网络ANSATZ，该网络扩展了量子量的体现函数的良好成熟的矩阵乘积状态表示。这种新结构提供了足够的资源，以确保尺寸大或相等的状态比一个遵守区域法的纠缠。可以通过优化方案进行有效操纵以解决基础搜索问题，该方案将张量 - 网络和各种蒙特卡洛算法混合。我们将新的ANSATZ基准在一个和二维中针对自旋模型进行基准测试，以表明高精度和精度。我们最终采用了我们的方法来研究具有挑战性的$ s = 1/2 $二维$ j_1 -j_2 $型号，表明它在2D中与最先进的方法具有竞争力。
By taking inspiration from the backflow transformation for correlated systems, we introduce a novel tensor network ansatz which extend the well-established Matrix Product State representation of a quantum-many body wave function. This new structure provides enough resources to ensure that states in dimension larger or equal than one obey an area law for entanglement. It can be efficiently manipulated to address the ground-state search problem by means of an optimization scheme which mixes tensor-network and variational Monte-Carlo algorithms. We benchmark the new ansatz against spin models both in one and two dimensions, demonstrating high accuracy and precision. We finally employ our approach to study the challenging $S=1/2$ two dimensional $J_1 - J_2$ model, demonstrating that it is competitive with the state of the art methods in 2D.
