标题： 其他地方的观察：通过重要性采样改进变分蒙特卡罗梯度 显示英文标题

标题： Looking elsewhere: improving variational Monte Carlo gradients by importance sampling

摘要： 神经网络量子态（NQS）为表示量子多体波函数提供了一种强大且富有表现力的假设。然而，通过变分蒙特卡洛（VMC）方法进行训练仍然具有挑战性。众所周知，某些情况——例如在量子化学中出现的尖峰波函数——会导致高方差梯度估计器，从而阻碍变分优化的有效性。在本工作中，我们通过自适应调整的重要采样方法系统地研究了应对这些采样问题的策略。我们的方法专门设计用于（i）针对梯度估计器而不是损失函数，（ii）不引入额外的超参数，以及（iii）计算成本低廉。我们在多种哈密顿量的基态搜索中对我们的方法进行了基准测试，包括受挫自旋系统和从头算量子化学。我们还在神经投影量子动力学的背景下展示了在保真度最小化方面的系统改进。总体而言，我们的方法可以显著降低原始VMC的计算成本，当针对高度尖峰的量子化学波函数时，最多可降低100倍。 显示英文摘要

摘要： Neural-network quantum states (NQS) offer a powerful and expressive ansatz for representing quantum many-body wave functions. However, their training via Variational Monte Carlo (VMC) methods remains challenging. It is well known that some scenarios - such as sharply peaked wave functions emerging in quantum chemistry - lead to high-variance gradient estimators hindering the effectiveness of variational optimizations. In this work we investigate a systematic strategy to tackle those sampling issues by means of adaptively tuned importance sampling. Our approach is explicitly designed to (i) target the gradient estimator instead of the loss function, (ii) not introduce additional hyperparameters and (iii) be computationally inexpensive. We benchmarked our approach across the ground-state search of a wide variety of hamiltonians, including frustrated spin systems and ab-initio quantum chemistry. We also show systematic improvements on the infidelity minimization in the context of neural projected quantum dynamics. Overall, our approach can reduce the computational cost of vanilla VMC considerably, up to a factor of 100x when targeting highly peaked quantum chemistry wavefunctions.