Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
Transferable Neural Wavefunctions for Solids
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Accurate Computation of Quantum Excited States with Neural Networks
Variational optimization of the amplitude of neural-network quantum many-body ground states
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.