Real time evolution with neural-network quantum states
Disordered Systems and Neural Networks
2022-01-26 v4 Strongly Correlated Electrons
Computational Physics
Quantum Physics
Abstract
A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit midpoint rule method, which preserves the symplectic form of Hamiltonian dynamics. We ensure that our complex-valued neural networks are holomorphic functions, and exploit this property to efficiently compute gradients. Application to the transverse-field Ising model on a one- and two-dimensional lattice exhibits an accuracy comparable to the stochastic configuration method proposed in [Carleo and Troyer, Science 355, 602-606 (2017)], but does not require computing the (pseudo-)inverse of a matrix.
Cite
@article{arxiv.1912.08831,
title = {Real time evolution with neural-network quantum states},
author = {Irene López Gutiérrez and Christian B. Mendl},
journal= {arXiv preprint arXiv:1912.08831},
year = {2022}
}
Comments
11 pages, 8 figures