Learning-Based Design of LQG Controllers in Quantum Coherent Feedback

[2502.08170] Learning-Based Design of LQG Controllers in Quantum Coherent Feedback Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2502.08170 (quant-ph) [Submitted on 12 Feb 2025 ( v1 ), last revised 23 Feb 2025 (this version, v2)] Title: Learning-Based Design of LQG Controllers in Quantum Coherent Feedback Authors: Chunxiang Song , Yanan Liu , Guofeng Zhang , Huadong Mo , Daoyi Dong View a PDF of the paper titled Learning-Based Design of LQG Controllers in Quantum Coherent Feedback, by Chunxiang Song and 4 other authors View PDF HTML (experimental) Abstract: In this paper, we propose a differential evolution (DE) algorithm specifically tailored for the design of Linear-Quadratic-Gaussian (LQG) controllers in quantum systems. Building upon the foundational DE framework, the algorithm incorporates specialized modules, including relaxed feasibility rules, a scheduled penalty function, adaptive search range adjustment, and the ``bet-and-run'' initialization strategy. These enhancements improve the algorithm's exploration and exploitation capabilities while addressing the unique physical realizability requirements of quantum systems. The proposed method is applied to a quantum optical system, where three distinct controllers with varying configurations relative to the plant are designed. The resulting controllers demonstrate superior performance, achieving lower LQG performance indices compared to existing approaches. Additionally, the algorithm ensures that the designs comply with physical realizability constraints, guaranteeing compatibility with practical quantum platforms. The proposed approach holds significant potential for application to other linear quantum systems in performance optimization tasks subject to physically feasible constraints. Subjects: Quantum Physics (quant-ph) ; Systems and Control (eess.SY) Cite as: arXiv:2502.08170 [quant-ph] (or arXiv:2502.08170v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2502.08170 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Published in IEEE Transactions on Cybernetics, 2026 Related DOI : https://doi.org/10.1109/TCYB.2026.3651242 Focus to learn more DOI(s) linking to related resources Submission history From: Chunxiang Song [ view email ] [v1] Wed, 12 Feb 2025 07:24:22 UTC (940 KB) [v2] Sun, 23 Feb 2025 06:47:30 UTC (941 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning-Based Design of LQG Controllers in Quantum Coherent Feedback, by Chunxiang Song and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-02 Change to browse by: cs cs.SY eess eess.SY References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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