Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks
Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks
复制标题
使用受限玻尔兹曼机神经网络精确表示多体交互
DOI：
10.1103/physreve.103.013302
复制
发表时间：
2021
影响因子：
2.4
通讯作者：
Roggero, Alessandro
中科院分区：
文献类型：
--
作者：
Rrapaj, Ermal;Roggero, Alessandro
Restricted Boltzmann machines (RBMs) 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 effective field theory 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.
