Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices

Abstract

The characterization of excited-state structure in semiconductor quantum dot (QD) devices is an important component of tuning them for spin-qubit operation. We present Spectroscopy With Intelligent Feature Tracking (SWIFT), a framework that combines machine-learning (ML)-assisted feature identification with physics-informed geometric processing to extract energy-level splittings from pulsed-gate spectroscopy data. SWIFT isolates the relevant spectral features and exploits their characteristic geometry to reduce the two-dimensional spectroscopy analysis to a one-dimensional peak-detection problem. It further combines an ensemble-based confidence metric with sequential accumulation of rapid, low-SNR scans, allowing the inferred spectrum to be reevaluated as experimental evidence accumulates. Using Si/SiGe QD devices, we demonstrate SWIFT both offline and in real time, including automated tracking of QD excited states and lead resonances. Benchmarking on 255 manually labeled scans shows that SWIFT reduces the median splitting error to 0.05 mV from 0.11 mV for a classical baseline, with the largest improvement on lower-quality measurements. These results provide a path toward incorporating excited-state spectroscopy into autonomous QD characterization, tuning, and optimization, which will be essential in large-scale quantum dot devices.