Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908807925334016 |
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| author | Muto, Yui Shinozaki, Motoya Yuta, Hideaki Tsuzuki, Tatsuo Taga, Kotaro Oiwa, Akira Fujita, Takafumi Otsuka, Tomohiro |
| author_facet | Muto, Yui Shinozaki, Motoya Yuta, Hideaki Tsuzuki, Tatsuo Taga, Kotaro Oiwa, Akira Fujita, Takafumi Otsuka, Tomohiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise Muto, Yui Shinozaki, Motoya Yuta, Hideaki Tsuzuki, Tatsuo Taga, Kotaro Oiwa, Akira Fujita, Takafumi Otsuka, Tomohiro Mesoscale and Nanoscale Physics 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. |
| title | Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2602.02922 |