论文标题
fermion标志波动的深度学习
Deep Learning of Fermion Sign Fluctuations
论文作者
论文摘要
我们描述了一种缓解费米符号问题的程序，其中从玻尔兹曼因子中明确减去了相位波动。设计和比较了几种用于波动的Ansätze。在没有足够高质量的ANSATZ的情况下，可以对神经网络进行训练以参数化波动。在$ 1+1 $尺寸的交错扭转模型上证明，我们检查了该方法的性能，因为使用了更深的神经网络，并与研究精心的轮廓变形方法结合使用。
We describe a procedure for alleviating the fermion sign problem in which phase fluctuations are explicitly subtracted from the Boltzmann factor. Several ansätze for fluctuations are designed and compared. In the absence of a sufficiently high-quality ansatz, a neural network can be trained to parameterize the fluctuations. Demonstrating on the staggered Thirring model in $1+1$ dimensions, we examine the performance of this method as deeper neural networks are used, and in conjunction with the well-studied contour deformation methods.
