Model-free, Learning-based Control of LGKS Quantum System

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dc.contributor.authorDelgado, Jhon Manuel Portella
dc.contributor.authorGoel, Ankit
dc.date.accessioned2024-11-14T15:18:39Z
dc.date.available2024-11-14T15:18:39Z
dc.date.issued2024-10-03
dc.description.abstractThis paper presents a model-free, learning-based adaptive controller for the density tracking problem in a two-level Lindblad-Gorini-Kossakowski-Sudarshan (LGKS) quantum system. The adaptive controller is based on the continuous-time retrospective cost adaptive control. To preserve the geometric properties of the quantum system, an adaptive PID controller driven and optimized by Ulhmann's fidelity is used. The proposed controller is validated in simulation for a low and a high-entropy density-tracking problem.
dc.description.urihttp://arxiv.org/abs/2410.02882
dc.format.extent8 pages
dc.genrejournal articles
dc.genrepreprints
dc.identifierdoi:10.13016/m2oxmv-e4ul
dc.identifier.urihttps://doi.org/10.48550/arXiv.2410.02882
dc.identifier.urihttp://hdl.handle.net/11603/36947
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Mechanical Engineering Department
dc.relation.ispartofUMBC Student Collection
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA 4.0 Deed
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
dc.subjectMathematics - Optimization and Control
dc.subjectUMBC Estimation, Control, and Learning Laboratory (ECLL).
dc.titleModel-free, Learning-based Control of LGKS Quantum System
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-2778-686X
dcterms.creatorhttps://orcid.org/0000-0002-4146-6275

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