Correlation-Enhanced Neural Networks as Interpretable Variational Quantum States

Autor:	Valenti, Agnes, Greplova, Eliska, Lindner, Netanel H., Huber, Sebastian D.
Rok vydání:	2021
Předmět:	Condensed Matter - Strongly Correlated Electrons Condensed Matter - Disordered Systems and Neural Networks Quantum Physics
Zdroj:	Phys. Rev. R 4, L012010 (2022)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevResearch.4.L012010
Popis:	Variational methods have proven to be excellent tools to approximate ground states of complex many body Hamiltonians. Generic tools like neural networks are extremely powerful, but their parameters are not necessarily physically motivated. Thus, an efficient parametrization of the wave-function can become challenging. In this letter we introduce a neural-network based variational ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect to the relevant correlations governing the physics of the system. We illustrate the success of this approach on topological, long-range correlated and frustrated models. Additionally, we introduce compatible variational optimization methods for exploration of low-lying excited states without symmetries that preserve the interpretability of the ansatz. Comment: 14 pages, 18 figures, code available at https://github.com/cmt-qo/cm-cRBM
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.05017 Zobrazit plný text záznamu View this record from Arxiv