Large Language Model Steering of Acousto-Optic Tweezer Arrays
Controlling quantum hardware requires choosing and revising experimental strategies as nonlinear response, coupling, and drift limit calibration. We experimentally investigate a general-purpose large language model (LLM) as a human-in-the-loop strategy layer for a neutral-atom tweezer platform. Through step-by-step interaction with experimental feedback, the LLM guides a transition from multiplicative row--column correction to a drift-corrected local response-matrix method when separable feedback stalls. Deterministic software performs image analysis and numerical updates, while an experimentalist verifies and authorizes each waveform. For a $10\times10$ crossed-acousto-optic-deflector array, this workflow reduces the Gaussian-peak coefficient of variation from $20.30\%$ to $1.57\%$ in 30 candidate-update rounds. The experiment demonstrates feedback-informed strategy selection on a physical quantum-control subsystem, rather than direct generation of control parameters alone. These results highlight the potential of LLMs to guide control optimization across diverse quantum platforms by adaptively formulating and refining experimental strategies.