论文标题
使用人工神经网络的典型解决方案的有效解决方案
Efficient Solutions of Fermionic Systems using Artificial Neural Networks
论文作者
论文摘要
我们讨论了使用基层波函数的常规和人工神经网络参数化的变异蒙特卡洛方法之间的差异和相似性。我们专注于相对较浅的神经网络架构，即所谓的限制性玻尔兹曼机器，并讨论适合模拟复杂的多体相关性的无监督学习算法。我们通过求解各种圆形量子点系统来分析常规和神经网络波函数的优势和劣势。提出了多达90个电子的结果，并特别强调如何有效地实施这些方法对均质和异质性高性能计算设施。
We discuss differences and similarities between variational Monte Carlo approaches that use conventional and artificial neural network parameterizations of the ground-state wave function for systems of fermions. We focus on a relatively shallow neural-network architectures, the so called restricted Boltzmann machine, and discuss unsupervised learning algorithms that are suitable to model complicated many-body correlations. We analyze the strengths and weaknesses of conventional and neural-network wave functions by solving various circular quantum-dots systems. Results for up to 90 electrons are presented and particular emphasis is placed on how to efficiently implement these methods on homogeneous and heterogeneous high-performance computing facilities.
