论文标题
具有图形神经ANSATZ的可扩展变异蒙特卡洛
Scalable variational Monte Carlo with graph neural ansatz
论文作者
论文摘要
深层神经网络已显示为在变异蒙特卡洛中潜在强大的ANSATZ，用于解决量子多体问题。我们提出了这一方向的两个改进。第一个是图形神经ANSATZ（GNA），它是多变量波函数通用到任意几何形状。 GNA在2D Kagome晶格，三角形晶格和随机连接的图上产生准确的地面能量。其次，我们在多个加速器上设计一个分布式工作流程以扩大计算。我们计算了128个TPU芯上的尺寸高达432个位置的Kagome Lattices。 GNA的参数共享性质还导致在不同系统大小和几何形状上的转移性。
Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this direction. The first is graph neural ansatz (GNA), which is a variational wavefunction universal to arbitrary geometry. GNA results in accurate ground-state energies on 2D Kagome lattices, triangular lattices, and randomly connected graphs. Secondly, we design a distributed workflow on multiple accelerators to scale up the computation. We compute Kagome lattices with sizes up to 432 sites on 128 TPU cores. The parameter sharing nature of the GNA also leads to transferability across different system sizes and geometries.
