Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization

Quantum Physics arXiv:2607.00284 (quant-ph) [Submitted on 1 Jul 2026] Title:Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization Authors:Caleb Walton, Patricia García-Caspueñas, Filippo Zacchei, Ana Larrañaga, Steven L. Brunton, Sara Mouradian View a PDF of the paper titled Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization, by Caleb Walton and 5 other authors View PDF Abstract:The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control. Comments: Subjects: Quantum Physics (quant-ph); Applied Physics (physics.app-ph) Cite as: arXiv:2607.00284 [quant-ph] (or arXiv:2607.00284v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.00284 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sara Mouradian [view email] [v1] Wed, 1 Jul 2026 00:17:40 UTC (5,250 KB) Full-text links: Access Paper: View a PDF of the paper titled Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization, by Caleb Walton and 5 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-07 Change to browse by: physics physics.app-ph 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?) 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?)