Data-driven learning of non-Markovian quantum dynamics
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Quantum Physics arXiv:2601.07934 (quant-ph) [Submitted on 12 Jan 2026] Title:Data-driven learning of non-Markovian quantum dynamics Authors:Samuel Goodwin (1,3), Brian K. McFarland (2), Manuel H. Muñoz-Arias (3), Edward C. Tortorici (2), Melissa C. Revelle (2), Christopher G. Yale (2), Daniel S. Lobser (2), Susan M. Clark (2), Mohan Sarovar (3) ((1) Department of Physics and Astronomy, Center for Quantum Information and Control, University of New Mexico, Albuquerque, New Mexico, (2) Sandia National Laboratories, Albuquerque, NM, (3) Quantum Algorithms and Applications Collaboratory, Sandia National Laboratories, Livermore, CA) View a PDF of the paper titled Data-driven learning of non-Markovian quantum dynamics, by Samuel Goodwin (1 and 20 other authors View PDF HTML (experimental) Abstract:Fault-tolerant quantum computing requires extremely precise knowledge and control of qubit dynamics during the application of a gate. We develop a data-driven learning protocol for characterizing quantum gates that builds off previous work on learning the Nakajima-Mori-Zwanzig (NMZ) formulation of open system dynamics from time series data, which allows detailed reconstruction of quantum evolution, including non-Markovian dynamics. We demonstrate this learning technique on three different systems: a simulation of a qubit whose dynamics are purely Markovian, a simulation of a driven qubit coupled to stochastic noise produced by an Ornstein-Uhlenbeck process, and trapped-ion experimental data of a driven qubit whose noise environment is not characterized ahead of time. Our technique is able to learn the generators of time evolution, or the NMZ operators, in all three cases and can learn the timescale in which the qubit dynamics can no longer be accurately described by a purely Markovian model. Our technique complements existing quantum gate characterization methods such as gate set tomography by explicitly capturing non-Markovianity in the gate generator, thus allowing for more thorough diagnosis of noise sources. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.07934 [quant-ph] (or arXiv:2601.07934v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.07934 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Samuel Goodwin [view email] [v1] Mon, 12 Jan 2026 19:03:58 UTC (1,381 KB) Full-text links: Access Paper: View a PDF of the paper titled Data-driven learning of non-Markovian quantum dynamics, by Samuel Goodwin (1 and 20 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?)