{"status":"public","project":[{"call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","grant_number":"665385"}],"oa_version":"Published Version","related_material":{"record":[{"id":"10762","status":"public","relation":"part_of_dissertation"},{"id":"7956","status":"public","relation":"part_of_dissertation"},{"status":"public","id":"415","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","status":"public","id":"8644"}]},"type":"dissertation","citation":{"apa":"Rzadkowski, W. (2022). <i>Analytic and machine learning approaches to composite quantum impurities</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:10759\">https://doi.org/10.15479/at:ista:10759</a>","ama":"Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:<a href=\"https://doi.org/10.15479/at:ista:10759\">10.15479/at:ista:10759</a>","ista":"Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria.","ieee":"W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022.","mla":"Rzadkowski, Wojciech. <i>Analytic and Machine Learning Approaches to Composite Quantum Impurities</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/at:ista:10759\">10.15479/at:ista:10759</a>.","chicago":"Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite Quantum Impurities.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/at:ista:10759\">https://doi.org/10.15479/at:ista:10759</a>.","short":"W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022."},"date_published":"2022-02-21T00:00:00Z","ddc":["530"],"year":"2022","publisher":"Institute of Science and Technology Austria","author":[{"first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech","last_name":"Rzadkowski"}],"date_created":"2022-02-16T13:27:37Z","oa":1,"abstract":[{"lang":"eng","text":"In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data."}],"publication_identifier":{"issn":["2663-337X"]},"title":"Analytic and machine learning approaches to composite quantum impurities","doi":"10.15479/at:ista:10759","article_processing_charge":"No","department":[{"_id":"GradSch"},{"_id":"MiLe"}],"has_accepted_license":"1","publication_status":"published","supervisor":[{"id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","first_name":"Mikhail","last_name":"Lemeshko","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail"}],"date_updated":"2024-08-07T07:16:53Z","degree_awarded":"PhD","month":"02","day":"21","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"10759","page":"120","alternative_title":["ISTA Thesis"],"ec_funded":1,"language":[{"iso":"eng"}],"file_date_updated":"2022-02-22T07:20:12Z","file":[{"content_type":"application/zip","file_name":"Rzadkowski_thesis_final_source.zip","creator":"wrzadkow","file_size":17668233,"access_level":"closed","checksum":"0fc54ad1eaede879c665ac9b53c93e22","date_created":"2022-02-21T13:58:16Z","file_id":"10785","relation":"source_file","date_updated":"2022-02-22T07:20:12Z"},{"file_size":13307331,"creator":"wrzadkow","content_type":"application/pdf","file_name":"Rzadkowski_thesis_final.pdf","success":1,"date_updated":"2022-02-21T14:02:54Z","date_created":"2022-02-21T14:02:54Z","checksum":"22d2d7af37ca31f6b1730c26cac7bced","relation":"main_file","file_id":"10786","access_level":"open_access"}]}