Large Language Model-Assisted Superconducting Qubit Experiments

TL;DR

This paper presents a framework that uses large language models to automate and streamline control and measurement procedures in superconducting qubit experiments, enhancing flexibility and ease of use.

Contribution

The work introduces a novel LLM-based framework for automating superconducting qubit experiments, enabling rapid deployment and flexible implementation of protocols.

Findings

Successfully characterized a superconducting resonator autonomously.

Reproduced a quantum non-demolition measurement from literature.

Demonstrated flexible, schema-less experimental control.

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.