Engineering quantum matter with generative machine learning

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKoch, Rouven Alexander
dc.contributor.departmentTeknillisen fysiikan laitosfi
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.labCorrelated Quantum Materials (CQM) groupen
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorLado, Jose L., Prof., Aalto University, Department of Applied Physics, Finland
dc.date.accessioned2024-05-31T09:00:17Z
dc.date.available2024-05-31T09:00:17Z
dc.date.defence2024-06-13
dc.date.issued2024
dc.description.abstractQuantum matter presents a rich landscape of emergent phenomena and exotic properties that are rare in natural compounds. This includes many-body systems such as topological insulators and unconventional superconductors. Understanding and characterizing these systems presents significant challenges due to their complexity and exotic behavior. In this dissertation, we explore the intersection of condensed matter theory, quantum matter, and artificial intelligence (AI). We demonstrate how machine learning (ML) can be used as a powerful tool for untangling complex problems in quantum many-body physics and go beyond conventional methods. Generative ML methods allow us to design complex quantum materials efficiently, optimize experimental parameters, uncover hidden correlations of quantum many-body systems, and bring together experiments and theoretical models. With this thesis, we aim to provide a complementary strategy to design exotic quantum phenomena, making a step towards future technological advancements in correlated quantum materials, materials science, and quantum computing.en
dc.format.extent142 + app. 80
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn978-952-64-1877-3 (electronic)
dc.identifier.isbn978-952-64-1876-6 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/128398
dc.identifier.urnURN:ISBN:978-952-64-1877-3
dc.language.isoenen
dc.opnPasserone, Daniele, Prof., Eidgenössische Technische Hochschule (ETH) Zurich, Switzerland
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Rouven Koch and Jose L. Lado. Neural network enhanced hybrid quantum many-body dynamical distributions. Physical Review Research, 3, 033102, July 2021. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202108048255. DOI: 10.1103/PhysRevResearch.3.033102
dc.relation.haspart[Publication 2]: Rouven Koch and Jose L. Lado. Designing quantum many-body matter with conditional generative adversarial networks. Physical Review Research, 4, 033223, September 2022. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202210196015. DOI: 10.1103/PhysRevResearch.4.033223
dc.relation.haspart[Publication 3]: Rouven Koch, David van Driel, Alberto Bordin, Jose L. Lado, and Eliska Greplova. Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain. Physical Review Applied, 20, 044081, October 2023. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202311086777. DOI: 10.1103/PhysRevApplied.20.044081
dc.relation.haspart[Publication 4]: Netta Karjalainen, Zina Lippo, Guangze Chen, Rouven Koch, Adolfo O. Fumega, and Jose L. Lado. Hamiltonian Inference from Dynamical Excitations in Confined Quantum Magnets. Physical Review Applied, 20, 024054, August 2023. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202308305311. DOI: 10.1103/PhysRevApplied.20.024054
dc.relation.haspart[Publication 5]: Maryam Khosravian, Rouven Koch, Jose L. Lado. Hamiltonian learning with real-space impurity tomography in topological moiré superconductors. Journal of Physics: Materials, 7, 015012 2024. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401312241. DOI: 10.1088/2515-7639/ad1c04
dc.relation.ispartofseriesAalto University publication series DOCTORAL THESESen
dc.relation.ispartofseries125/2024
dc.revCalderon Prieto, Maria Jose, Dr., Consejo Superior de Investigaciones Cientificas (CSIC) Madrid, Spain
dc.revRubio Verdú, Carmen, Dr., Instituto de Ciencias Fotónicas (ICFO) Barcelona, Spain
dc.subject.keywordCorrelated quantum materialsen
dc.subject.keywordgenerative machine learningen
dc.subject.keywordquantum many-body physicsen
dc.subject.otherMaterials scienceen
dc.titleEngineering quantum matter with generative machine learningen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2024-06-13_1132
local.aalto.archiveyes
local.aalto.formfolder2024_05_30_klo_13_11
local.aalto.infraScience-IT
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