Large Language Model-Assisted Superconducting Qubit Experiments

TL;DR

This work introduces a framework that leverages a large language model (LLM) to automate qubit control and measurement and conducts experiments by generating and invoking schema-less tools on demand via a knowledge base on instrumental usage and experimental procedures.

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.

Figures (5)

• Figure 1: Schematic overview of the experimental setup. The measurement system is organized into a multi-layer architecture that bridges high-level user intent with physical quantum circuits. Via the experiment computer, both users and AI systems can interact with specialized control packages, most notably the open-source package QuICK github_quick. The experiment computer is connected to a local area network (LAN), through which commands are transmitted to a suite of instruments, which synthesize and detect analog RF and DC signals routed to a dilution refrigerator (DR) to interact with superconducting circuits at cryogenic temperatures. Experimental progress and results are managed by a centralized storage server and visualized in real-time through the open-source Grapher software github_grapher. In practice, multiple sets of computers, instruments, and DRs are connected to the same LAN to allow convenient and simultaneous experiments across the laboratory.
• Figure 2: Architecture of Heuristic Autonomous Lab (HAL). (a) Workflow: The system operates in a cycle with multiple steps, supported by long-term (green) and short-term (amber) contexts. High-thinking steps (purple) involve complex tasks including planning and development, while low-thinking steps (blue) handle routine tasks like searching. (b) Search Agent: Iterative retrieval-augmented generation (RAG) process to accurately find relevant documents from the knowledge base using vectorization and cosine similarity, effectively handling references among documents by iteration. (c) Execution Runtime: A sandbox environment, where the generated code is invoked, maintains a persistent state across executions and allows the invocation of other codes. (d) Signal Pathway: A feedback mechanism to close the information loop, allowing the AI system to acquire critical information about the execution results.
• Figure 3: Autonomous resonator characterization. (a) Summary of the user input (blue), performed actions (purple), STATE read and write (black), and resulting signals (red) across five cycles. Cycles 2, 4, and 5 run without any user input. (b) Measured spectrum corresponding to the vector network analyzer (VNA) scan in cycle 1 (orange) and cycle 3 (blue). Pink spans are the resonators identified after cycle 5. Inset shows a representative circle fit performed during cycle 5 megrant2012planar.
• Figure 4: Implementation of a QND characterization from a published journal article hazra2025benchmarking. (a) Workflow for translating scientific literature into a working experiment. A journal article is processed by an LLM chatbot to generate lab-independent instructions, which are then refined into lab-specific instructions by the HAL answer component, guiding HAL on how to conduct the experiment. (b) Experimental results of a QND characterization sequence. The main plot shows the average correlation as a function of the readout cycle index. Data (blue) is fitted against the decay model hazra2025benchmarking, where a leakage rate from the computational basis of the qubit $L = 0.124\pm0.017$ is extracted from the fit (red). Inset illustrates the pulse sequence (not to scale), showing the randomized qubit control $\pi$ pulses (amber) and readout pulses (green).
• Figure 5: Readout fidelity metrics sunada2022fastswiadek2024enhancinghazra2025benchmarking (visibility, repeatability, and complement of leakage ($1-L$)) as a function of readout power, the latter in units of the QICK board channel gain. The solid lines are polylines for visual guidance. The plot shows improvement of visibility and repeatability with readout power until near the maximum shown here, but general degradation due to increased leakage as power is increased.