Nys Jannes, Pescia Gabriel, Sinibaldi Alessandro, Carleo Giuseppe
Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Center for Quantum Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Nat Commun. 2024 Oct 30;15(1):9404. doi: 10.1038/s41467-024-53672-w.
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our work introduces a variational approach for fermionic time-dependent wave functions, surpassing mean-field approximations by accurately capturing many-body correlations. We employ time-dependent Jastrow factors and backflow transformations, enhanced through neural networks parameterizations. To compute the optimal time-dependent parameters, we employ the time-dependent variational Monte Carlo technique and introduce a new method based on Taylor-root expansions of the propagator, enhancing the accuracy of our simulations. The approach is demonstrated in three distinct systems. In all cases, we show clear signatures of many-body correlations in the dynamics. The results showcase the ability of our variational approach to accurately describe the time evolution, providing insight into quantum dynamical effects in interacting electronic systems, beyond the capabilities of mean-field.
理解多电子量子系统的实时演化对于研究凝聚态物质、量子化学和复杂材料中的动力学性质至关重要，但这带来了重大的理论和计算挑战。我们的工作引入了一种用于费米子含时波函数的变分方法，通过精确捕捉多体关联超越了平均场近似。我们采用含时的贾斯特罗因子和回流变换，并通过神经网络参数化进行增强。为了计算最优的含时参数，我们采用含时变分蒙特卡罗技术，并引入了一种基于传播子泰勒根展开的新方法，提高了模拟的准确性。该方法在三个不同的系统中得到了验证。在所有情况下，我们都展示了动力学中多体关联的清晰特征。结果表明我们的变分方法能够准确描述时间演化，深入了解相互作用电子系统中的量子动力学效应，这是平均场方法所无法做到的。
