{"external_id":{"arxiv":["2105.15193"]},"publication_status":"submitted","title":"Artificial neural network states for non-additive systems","ec_funded":1,"status":"public","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_created":"2022-02-17T11:18:57Z","oa_version":"Preprint","language":[{"iso":"eng"}],"date_published":"2021-05-31T00:00:00Z","month":"05","type":"preprint","year":"2021","day":"31","_id":"10762","publication":"arXiv","abstract":[{"text":"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.","lang":"eng"}],"project":[{"call_identifier":"H2020","name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425","grant_number":"801770"},{"grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","call_identifier":"H2020"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2105.15193"}],"date_updated":"2023-09-07T13:44:16Z","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"10759"}]},"department":[{"_id":"MiLe"}],"acknowledgement":"We acknowledge fruitful discussions with Giacomo Bighin, Giammarco Fabiani, Areg Ghazaryan, Christoph\r\nLampert, and Artem Volosniev at various stages of this work. W.R. is a recipient of a DOC Fellowship of the\r\nAustrian Academy of Sciences and has received funding from the EU Horizon 2020 programme under the Marie\r\nSkłodowska-Curie Grant Agreement No. 665385. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). This work is part of the Shell-NWO/FOM-initiative “Computational sciences for energy research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences, FOM and STW.","page":"2105.15193","author":[{"last_name":"Rzadkowski","full_name":"Rzadkowski, Wojciech","orcid":"0000-0002-1106-4419","id":"48C55298-F248-11E8-B48F-1D18A9856A87","first_name":"Wojciech"},{"full_name":"Lemeshko, Mikhail","last_name":"Lemeshko","first_name":"Mikhail","orcid":"0000-0002-6990-7802","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Mentink","full_name":"Mentink, Johan H.","first_name":"Johan H."}],"citation":{"apa":"Rzadkowski, W., Lemeshko, M., &#38; Mentink, J. H. (n.d.). Artificial neural network states for non-additive systems. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2105.15193\">https://doi.org/10.48550/arXiv.2105.15193</a>","mla":"Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/arXiv.2105.15193\">10.48550/arXiv.2105.15193</a>.","ieee":"W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” <i>arXiv</i>. .","chicago":"Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Non-Additive Systems.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2105.15193\">https://doi.org/10.48550/arXiv.2105.15193</a>.","ama":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2105.15193\">10.48550/arXiv.2105.15193</a>","short":"W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.).","ista":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, <a href=\"https://doi.org/10.48550/arXiv.2105.15193\">10.48550/arXiv.2105.15193</a>."},"doi":"10.48550/arXiv.2105.15193"}