Large Language Model-Assisted Superconducting Qubit Experiments

2026-03-09

AI Summary

This work addresses the complexity and high expertise barrier in controlling and measuring superconducting qubit experiments, which hinder experimental efficiency and accessibility. We present the first integration of large language models into this domain by developing a knowledge-base-driven automation framework that enables autonomous operation of superconducting quantum hardware. The framework dynamically generates schema-free tools on demand and interfaces directly with instrument control APIs, supporting both rapid deployment of standard protocols and flexible design of novel experiments—significantly lowering the technical entry barrier. Experimentally, the system autonomously performed resonator characterization and successfully reproduced quantum non-demolition (QND) measurements reported in the literature, demonstrating the framework’s effectiveness and generalizability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• superconducting qubit
• quantum control
• experimental automation
• measurement protocols
• quantum hardware

Innovation

• Large Language Model
• Superconducting Qubit
• Automated Experimentation
• Schema-less Tool Generation
• Quantum Control