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
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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
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View a PDF of the paper titled Large Language Model-Assisted Superconducting Qubit Experiments, by Shiheng Li and 16 other authors
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