论文标题
元变异蒙特卡洛
Meta Variational Monte Carlo
论文作者
论文摘要
在元学习与确定从已知合奏中绘制的随机产生的哈密顿量的基态的问题之间发现了鉴定。提出了一种模型远程学习方法来解决相关的学习问题，并且对随机最大切割问题的初步实验研究表明，所得的元变异蒙特卡洛会加速训练并改善收敛性。
An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and a preliminary experimental study of random Max-Cut problems indicates that the resulting Meta Variational Monte Carlo accelerates training and improves convergence.
