Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
This provides a computational method for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, representing a novel method for a known bottleneck.
The paper tackles the challenge of simulating real-time evolution in many-electron quantum systems by introducing a variational approach with neural network-enhanced Jastrow factors and backflow transformations, which accurately captures many-body correlations beyond mean-field approximations in three distinct systems.
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. Therefore, we employ time-dependent Jastrow factors and backflow transformations, which are enhanced through neural networks parameterizations. To compute the optimal time-dependent parameters, we utilize the time-dependent variational Monte Carlo technique and 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 capture the time evolution, providing insight into the quantum dynamics of interacting electronic systems, beyond the capabilities of mean-field.