Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks

Autor:	Roth, Christopher
Rok vydání:	2020
Předmět:	Physics - Computational Physics Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Strongly Correlated Electrons
Druh dokumentu:	Working Paper
Popis:	Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size. Finding efficient compressions of the wavefunction is key to building scalable models. Here, we introduce iterative retraining, an approach for simulating bulk quantum systems that uses recurrent neural networks (RNNs). By mapping translations in the lattice vector to the time index of an RNN, we are able to efficiently capture the near translational invariance of large lattices. We show that we can use this symmetry mapping to simulate very large systems in one and two dimensions. We do so by 'growing' our model, iteratively retraining the same model on progressively larger lattices until edge effects become negligible. We argue that this scheme generalizes more naturally to higher dimensions than Density Matrix Renormalization Group. Comment: 7 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.06228 Zobrazit plný text záznamu View this record from Arxiv