LLM-assisted Superconducting Qubit Experiment

Quantum systems based on superconducting qubits have significant potential in quantum information 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. Here we introduce a framework that leverages an artificial intelligence Large Language Model (LLM) to implement and streamline this type of experimental control. By combining LLM's advanced coding capabilities with a knowledge base, we demonstrate autonomous superconducting qubit experiments that are easily integrated with our existing lab infrastructure. This framework enables both rapid deployment of standard control-and-measurement protocols as well as providing easy flexibility for novel experimental procedures, providing a more agile and intuitive paradigm for controlling complex quantum hardware.