通过避免统计困难参数改进变分Monte Carlo优化
Improving Variational Monte Carlo Optimization by Avoiding Statistically Difficult Parameters
摘要：
现代量子蒙特卡罗（QMC）方法通常通过明确关联Jastrow因子和中小组态相互作用（CI）展开来捕获电子关联。在这里，我们研究了增加CI参数数量所带来的额外优化难度。我们发现的证据表明，变分蒙特卡罗（VMC）优化的质量可以限制的能力，在Jastrow因子的存在下，统计解决的CI参数。虽然使用更大的统计样本可以缓解这个问题，并使优化更接近其真正的最小值，但这种方法是计算密集型的。我们提出的证据表明，类似的收益，优化质量，可以避免CI参数，统计上最难解决，而不增加样本量。我们的研究结果表明，除了使用传统的选择组态相互作用（sCI）的方法来准备VMC波函数的价值，sCI类方法可以发挥重要作用，在VMC通过提高随机能量最小化的有效性。
英文摘要：
Modern quantum Monte Carlo (QMC) methods often capture electron correlation through both explicitly correlating Jastrow factors and small to mid-sized configuration interaction (CI) expansions. Here, we study the additional optimization difficulty created by including increasing numbers of CI parameters. We find evidence that the quality of Variational Mone Carlo (VMC) optimization can be limited by the ability to statistically resolve the CI parameters in the presence of a Jastrow factor. Although using larger statistical samples can mitigate this issue and bring an optimization closer to its true minimum, this approach is computationally intensive. We present evidence that similar gains to optimization quality can be had without increasing the sample size by avoiding CI parameters that are statistically the most difficult to resolve. Our findings suggest that, in addition to the value of using traditional selected configuration interaction (sCI) methods to prepare VMC wave functions, sCI-like methods can play an important role within VMC by improving the effectiveness of stochastic energy minimization.
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