Single-shot spin-state discrimination is essential for semiconductor spin qubits, but conventional threshold-based analysis of spin readout traces becomes unreliable under noisy conditions. Although recent neural-network-based methods improve robustness against experimental noise, they are sensitive to training conditions, restricted to fixed-length inputs, and limited to trace-level outputs without explicit temporal localization of transition events. In this work, we apply a U-Net architecture to spin readout signal analysis by formulating transition-event detection as a point-wise segmentation task in one-dimensional time-series data. The fully convolutional structure enables direct processing of variable-length traces. Point-wise and sample-wise evaluations demonstrate low readout error rates and high classification accuracy without retraining. The proposed method generalizes well to previously-unseen trace lengths and experimental non-Gaussian noise, outperforming a conventional threshold-based approach and providing a robust and practical solution for automated spin readout signal analysis.
@article{arxiv.2602.02922,
title = {Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise},
author = {Yui Muto and Motoya Shinozaki and Hideaki Yuta and Tatsuo Tsuzuki and Kotaro Taga and Akira Oiwa and Takafumi Fujita and Tomohiro Otsuka},
journal= {arXiv preprint arXiv:2602.02922},
year = {2026}
}