论文标题
优化量子蒙特卡洛的大型决定因素扩张
Optimization of large determinant expansions in quantum Monte Carlo
论文作者
论文摘要
我们提出了一种在量子蒙特卡洛（QMC）框架中优化大型配置相互作用（CI）扩展的新方法。这里的核心思想是要替换通常通过正交非官方非官员优化在常规QMC计算中对CI系数的非正交变化优化，这要归功于所谓的超相关（TC）框架，即在完整基集的限制中产生相同结果的两种方法。通过将TC方程作为一个有效的自一致的遗传学问题重写，我们的方法需要对每个Slater决定因素进行单个数量的采样，从而在QMC代码中最少记忆要求。使用从TC框架和通常的CI-Type计算获得的分析量，我们还提出了改进的估计量，从而将采样数量的统计波动降低了超过一个数量级。我们使用有效的核心电位或全电子计算，证明了该方法对包含$ 10^5-10^6 $ slater决定因素的波函数的效率。在所有情况下，仅在两到三个优化的迭代中就达到了亚米哈特蛋白的收敛。
We present a new method for the optimization of large configuration interaction (CI) expansions in the quantum Monte Carlo (QMC) framework. The central idea here is to replace the non-orthogonal variational optimization of CI coefficients performed in usual QMC calculations by an orthogonal non-Hermitian optimization thanks to the so-called transcorrelated (TC) framework, the two methods yielding the same results in the limit of a complete basis set. By rewriting the TC equations as an effective self-consistent Hermitian problem, our approach requires the sampling of a single quantity per Slater determinant, leading to minimal memory requirements in the QMC code. Using analytical quantities obtained from both the TC framework and the usual CI-type calculations, we also propose improved estimators which reduce the statistical fluctuations of the sampled quantities by more than an order of magnitude. We demonstrate the efficiency of this method on wave functions containing $10^5-10^6$ Slater determinants, using effective core potentials or all-electron calculations. In all the cases, a sub-milliHartree convergence is reached within only two or three iterations of optimization.
