Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise

TL;DR

This paper introduces a U-Net based neural network for analyzing noisy, variable-length spin readout signals, enabling accurate, automated detection of transition events in quantum computing experiments.

Contribution

The work presents a novel application of U-Net architecture for point-wise segmentation of spin readout traces, handling variable lengths and noise without retraining, improving robustness over existing methods.

Findings

Achieves low readout error rates and high classification accuracy.

Generalizes well to unseen trace lengths and non-Gaussian noise.

Outperforms conventional threshold-based analysis methods.

Abstract

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.