--- res: bibo_abstract: - Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Wojciech foaf_name: Rzadkowski, Wojciech foaf_surname: Rzadkowski foaf_workInfoHomepage: http://www.librecat.org/personId=48C55298-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-1106-4419 - foaf_Person: foaf_givenName: Mikhail foaf_name: Lemeshko, Mikhail foaf_surname: Lemeshko foaf_workInfoHomepage: http://www.librecat.org/personId=37CB05FA-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-6990-7802 - foaf_Person: foaf_givenName: Johan H. foaf_name: Mentink, Johan H. foaf_surname: Mentink bibo_doi: 10.48550/arXiv.2105.15193 dct_date: 2021^xs_gYear dct_language: eng dct_title: Artificial neural network states for non-additive systems@ ...