Harnessing Environmental Memory with Reinforcement Learning in Open Quantum Systems

Quantum Physics arXiv:2601.01252 (quant-ph) [Submitted on 3 Jan 2026] Title:Harnessing Environmental Memory with Reinforcement Learning in Open Quantum Systems Authors:Safae Gaidi, Abdallah Slaoui, Mohammed EL Falaki, Amine Jaouadi View a PDF of the paper titled Harnessing Environmental Memory with Reinforcement Learning in Open Quantum Systems, by Safae Gaidi and 2 other authors View PDF HTML (experimental) Abstract:Non-Markovian memory effects in open quantum systems provide valuable resources for preserving coherence and enhancing controllability. However, exploiting them requires strategies adapted to history-dependent dynamics. We introduce a reinforcement-learning framework that autonomously learns to amplify information backflow in a driven two-level system coupled to a structured reservoir. Using a reward based on the positive time derivative of the trace distance associated with the Breuer-Laine-Piilo measure, we train PPO and SAC agents and benchmark their performance against gradient-based optimal control theory (OCT). While OCT enhances a single dominant backflow peak, RL policies broaden this revival and activate additional contributions in later memory windows, producing sustained positive trace-distance growth over a longer duration. Consequently, the integrated non-Markovianity achieved by RL substantially exceeds that obtained with OCT. These results demonstrate how long-horizon, model-free learning naturally uncovers distributed-backflow strategies and highlight the potential of reinforcement learning for engineering memory effects in open quantum systems. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.01252 [quant-ph] (or arXiv:2601.01252v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.01252 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Amine Jaouadi Dr. [view email] [v1] Sat, 3 Jan 2026 18:22:33 UTC (1,138 KB) Full-text links: Access Paper: View a PDF of the paper titled Harnessing Environmental Memory with Reinforcement Learning in Open Quantum Systems, by Safae Gaidi and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?)