Large Language Model-Assisted Superconducting Qubit Experiments
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arXiv
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| Format: | Preprint |
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2026
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| author | Li, Shiheng Miller, Jacob M. Lee, Phoebe J. Andersson, Gustav Conner, Christopher R. Joshi, Yash J. Karimi, Bayan King, Amber M. Malc, Howard L. Mishra, Harsh Qiao, Hong Ryu, Minseok Wu, Xuntao Xing, Siyuan Yan, Haoxiong Shi, Jian Cleland, Andrew N. |
| author_facet | Li, Shiheng Miller, Jacob M. Lee, Phoebe J. Andersson, Gustav Conner, Christopher R. Joshi, Yash J. Karimi, Bayan King, Amber M. Malc, Howard L. Mishra, Harsh Qiao, Hong Ryu, Minseok Wu, Xuntao Xing, Siyuan Yan, Haoxiong Shi, Jian Cleland, Andrew N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08801 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large Language Model-Assisted Superconducting Qubit Experiments Li, Shiheng Miller, Jacob M. Lee, Phoebe J. Andersson, Gustav Conner, Christopher R. Joshi, Yash J. Karimi, Bayan King, Amber M. Malc, Howard L. Mishra, Harsh Qiao, Hong Ryu, Minseok Wu, Xuntao Xing, Siyuan Yan, Haoxiong Shi, Jian Cleland, Andrew N. Quantum Physics Artificial Intelligence 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. |
| title | Large Language Model-Assisted Superconducting Qubit Experiments |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.08801 |