Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics
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Quantum Physics arXiv:2602.04480 (quant-ph) [Submitted on 4 Feb 2026] Title:Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics Authors:JunDong Zhong, ZhaoMing Wang View a PDF of the paper titled Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics, by JunDong Zhong and ZhaoMing Wang View PDF HTML (experimental) Abstract:The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically requires numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM-NNs) can accurately predict the time evolution of open quantum systems. Based on LSTM-NN predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As an exemplary example, we apply our scheme to design an optimal control for the adiabatic speedup in a two-level system under a non-Markovian environment. Our optimization procedure entails two steps: driving trajectory optimization and zero-area pulse optimization. Fidelity improvement for both steps have been obtained, showing the effectiveness of the scheme. Our optimal control design scheme utilizes predicted dynamics to generate optimized controls, offering broad application potential in quantum computing, communication, and sensing. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.04480 [quant-ph] (or arXiv:2602.04480v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.04480 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhaoming Wang [view email] [v1] Wed, 4 Feb 2026 12:08:12 UTC (1,208 KB) Full-text links: Access Paper: View a PDF of the paper titled Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics, by JunDong Zhong and ZhaoMing WangView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-02 References & Citations INSPIRE HEP NASA ADSGoogle 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)