Stochastic representation of time-evolving neural network-based wavefunctions

Abstract

Solving the time-dependent Schrödinger equation (TDSE) is pivotal for modeling nonadiabatic electron dynamics, a key process in ultrafast spectroscopy and laser–matter interactions. However, exact solutions to the TDSE remain computationally prohibitive for most realistic systems, as the Hilbert space expands exponentially with dimensionality. In this work, we propose an approach integrating the stochastic representation framework with a neural network wavefunction ansatz, a flexible model capable of approximating time-evolving quantum wavefunctions. We first validate the method on one-dimensional, single-electron systems, focusing on ionization dynamics under intense laser fields, a critical process in attosecond physics. Our results demonstrate that the approach accurately reproduces key features of quantum evolution, including the energy and dipole evolution during ionization. We further show the feasibility of extending this approach to three-dimensional systems. Due to the increased complexity of real-time simulations in higher dimensions, these results remain at an early stage and highlight the need for more advanced stabilization strategies.