论文标题
卷积限制的鲍尔茨曼机器协助蒙特卡洛：伊辛和基塔维尔型号的应用
Convolutional restricted Boltzmann machine aided Monte Carlo: An application to Ising and Kitaev models
论文作者
论文摘要
机器学习已广泛用于分析多体冷凝物质系统的热力学。受限的玻尔兹曼机器（RBM）辅助蒙特卡洛模拟最近引起了人们的兴趣，因为它们设法加快了经典的蒙特卡洛模拟。在这里，我们采用了卷积限制的玻尔兹曼机器（CRBM）方法，并证明其使用有助于通过利用翻译不变性来大大学习的参数数量。此外，我们表明可以以较小的晶格尺寸训练CRBM，并将其应用于较大的晶格尺寸。为了证明CRBM的效率，我们将其应用于二维中的范式Ising和Kitaev模型。
Machine learning is becoming widely used in analyzing the thermodynamics of many-body condensed matter systems. Restricted Boltzmann Machine (RBM) aided Monte Carlo simulations have sparked interest recently, as they manage to speed up classical Monte Carlo simulations. Here we employ the Convolutional Restricted Boltzmann Machine (CRBM) method and show that its use helps to reduce the number of parameters to be learned drastically by taking advantage of translation invariance. Furthermore, we show that it is possible to train the CRBM at smaller lattice sizes, and apply it to larger lattice sizes. To demonstrate the efficiency of CRBM we apply it to the paradigmatic Ising and Kitaev models in two-dimensions.
