论文标题
许多身体相互作用与RBM神经网络的确切表示
Exact representations of many body interactions with RBM neural networks
论文作者
论文摘要
受限的玻尔兹曼机器（RBM）是在两分图上定义的简单统计模型，该模型已成功地用于研究更复杂的多体系统，包括经典和量子。在这项工作中，我们利用RBMS的表示能力将多体接触相互作用的确切分解为单体操作员与离散辅助场耦合。这种结构概括了众所周知的Hirsch模型用于更复杂的理论，例如核物理学中的无疑EFT，我们将详细分析。我们还讨论了映射在量子退火应用程序中的可能应用，并通过机器学习对RBM参数优化产生了一些影响。
Restricted Boltzmann Machines (RBM) are simple statistical models defined on a bipartite graph which have been successfully used in studying more complicated many-body systems, both classical and quantum. In this work, we exploit the representation power of RBMs to provide an exact decomposition of many-body contact interactions into one-body operators coupled to discrete auxiliary fields. This construction generalizes the well known Hirsch's transform used for the Hubbard model to more complicated theories such as Pionless EFT in nuclear physics, which we analyze in detail. We also discuss possible applications of our mapping for quantum annealing applications and conclude with some implications for RBM parameter optimization through machine learning.
