{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:14Z","timestamp":1760145674378,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>This paper investigates a quantum system described by the Schr\u00f6dinger equation, utilizing the concept of the quantum Lyapunov function. The Lyapunov function is chosen based on the mean value of a virtual mechanical quantity, where different values of P, the mean value of the virtual mechanical quantity in the Lyapunov function, have an impact on the attractive domain of the quantum system. The selected primary optimization algorithms approximating matrix P are the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm. This study examines the characteristics of the system\u2019s attraction domain under these two distinct algorithms and establishes stability conditions for the nonlinear quantum system. We introduce a method to estimate the size of the attractive domain using the Lyapunov function approach, converting the attractive domain issue into an optimization challenge. Numerical simulations are conducted in various two-dimensional test systems and spin 1\/2 particle systems.<\/jats:p>","DOI":"10.3390\/axioms13080542","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:23:46Z","timestamp":1723461826000},"page":"542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of the Attraction Domain for the Quantum Systems Based on the Schr\u00f6dinger Equation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8259-5241","authenticated-orcid":false,"given":"Hongli","family":"Yang","sequence":"first","affiliation":[{"name":"College of Big Data, Qingdao Huanghai University, Qingdao 266427, China"},{"name":"College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Guohui","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9019-072X","authenticated-orcid":false,"given":"Ivan Ganchev","family":"Ivanov","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Business Administration, St. Kl.Ohridski Sofia University, 1113 Sofia, Bulgaria"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1002\/asjc.2943","article-title":"Enhancing topological information of the Lyapunov-based distributed model predictive control design for large-scale nonlinear systems","volume":"25","author":"He","year":"2022","journal-title":"Asian J. 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