Paths towards time evolution with larger neural-network quantum states

Autor:	Zhang, Wenxuan, Xing, Bo, Xu, Xiansong, Poletti, Dario
Rok vydání:	2024
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Quantum Gases Condensed Matter - Statistical Mechanics
Druh dokumentu:	Working Paper
Popis:	In recent years, the neural-network quantum states method has been investigated to study the ground state and the time evolution of many-body quantum systems. Here we expand on the investigation and consider a quantum quench from the paramagnetic to the anti-ferromagnetic phase in the tilted Ising model. We use two types of neural networks, a restricted Boltzmann machine and a feed-forward neural network. We show that for both types of networks, the projected time-dependent variational Monte Carlo (p-tVMC) method performs better than the non-projected approach. We further demonstrate that one can use K-FAC or minSR in conjunction with p-tVMC to reduce the computational complexity of the stochastic reconfiguration approach, thus allowing the use of these techniques for neural networks with more parameters. Comment: 13 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.03381 Zobrazit plný text záznamu View this record from Arxiv