神经网络变分蒙特卡罗的方差外推方法
Variance extrapolation method for neural-network variational Monte Carlo
AI总结：
本文提出一种方差外推方法，通过利用训练数据加速神经网络变分蒙特卡罗的收敛，突破 ansatz 限制以获得更精确的能量估计，并显著提升相对能量结果的误差抵消能力。
摘要：
构建更具表达力的 ansatz 一直是量子蒙特卡罗的重点，旨在实现更精确的 ab initio 计算。然而，随着 ansatz 更加强大，例如近期基于神经网络架构的各种模型，训练变得更加困难且昂贵，这可能对计算精度产生反作用。在本工作中，我们提出在使用神经网络 ansatz 进行变分蒙特卡罗时，利用训练数据执行方差外推。我们表明该方法能加速收敛并突破 ansatz 限制，从而获得改进的能量估计。此外，方差外推极大增强了误差抵消能力，从而显著改善了相对能量结果，这是化学与物理问题的关键。
英文摘要：
Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate \textit{ab initio} calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, which may have a counterproductive effect on the accuracy of calculation. In this work, we propose to make use of the training data to perform variance extrapolation when using neural-network ansatz in variational Monte Carlo. We show that this approach can speed up the convergence and surpass the ansatz limitation to obtain an improved estimation of the energy. Moreover, variance extrapolation greatly enhances the error cancellation capability, resulting in significantly improved relative energy outcomes, which are the keys to chemistry and physics problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
