Large Language Model-Assisted Superconducting Qubit Experiments

Quantum Physics arXiv:2603.08801 (quant-ph) [Submitted on 9 Mar 2026] Title:Large Language Model-Assisted Superconducting Qubit Experiments Authors:Shiheng Li, Jacob M. Miller, Phoebe J. Lee, Gustav Andersson, Christopher R. Conner, Yash J. Joshi, Bayan Karimi, Amber M. King, Howard L. Malc, Harsh Mishra, Hong Qiao, Minseok Ryu, Xuntao Wu, Siyuan Xing, Haoxiong Yan, Jian Shi, Andrew N. Cleland View a PDF of the paper titled Large Language Model-Assisted Superconducting Qubit Experiments, by Shiheng Li and 16 other authors View PDF HTML (experimental) Abstract:Superconducting circuits have demonstrated significant potential in quantum information processing and quantum sensing. Implementing novel control and measurement sequences for superconducting qubits is often a complex and time-consuming process, requiring extensive expertise in both the underlying physics and the specific hardware and software. In this work, we introduce a framework that leverages a large language model (LLM) to automate qubit control and measurement. Specifically, our framework conducts experiments by generating and invoking schema-less tools on demand via a knowledge base on instrumental usage and experimental procedures. We showcase this framework with two experiments: an autonomous resonator characterization and a direct reproduction of a quantum non-demolition (QND) characterization of a superconducting qubit from literature. This framework enables rapid deployment of standard control-and-measurement protocols and facilitates implementation of novel experimental procedures, offering a more flexible and user-friendly paradigm for controlling complex quantum hardware. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.08801 [quant-ph] (or arXiv:2603.08801v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.08801 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shiheng Li [view email] [v1] Mon, 9 Mar 2026 18:03:10 UTC (4,917 KB) Full-text links: Access Paper: View a PDF of the paper titled Large Language Model-Assisted Superconducting Qubit Experiments, by Shiheng Li and 16 other authorsView PDFHTML (experimental)TeX Source view license Ancillary-file links: Ancillary files (details): supplement.pdf Current browse context: quant-ph new | recent | 2026-03 Change to browse by: cs cs.AI 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?)