arXiv subjects

Yui Muto

Publications and source records attributed to Yui Muto.

5 recordsLinked to original sources

arXiv2026

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

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.

cond-mat.mes-hall↗

arXiv2025

Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices.

cond-mat.mes-hall↗

arXiv2024

Charge state estimation in quantum dots using a Bayesian approach

Detection of single-electron charges in solid-state nanodevices is a key technique in semiconductor quantum bit readout for quantum information processing and probing electronic properties of nanostructures. This detection is achieved using quantum dot charge sensors, with its speed enhanced by high-speed RF reflectometry. Recently, real-time processing of data from RF reflectometry has attracted much attention to quantum information processing. In this paper, we propose a sequential method based on Bayes' theorem for estimating the charge state and compare its performance with the averaging approach and threshold judgment. When the noise variance differs between the empty and occupied states, the Bayesian approach demonstrates a lower error score, facilitating the extraction of more data points in real-time charge state estimation. Additionally, the Bayesian approach outperforms the averaging method and threshold judgment in terms of error rates for charge state estimation, even during charge transitions. This technique is broadly applicable to single-electron detection and holds substantial utility for quantum bit readout and the operation of nanoprobes that utilize single-electron detection.

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arXiv2022

Visual explanations of machine learning model estimating charge states in quantum dots

Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learning model, which is usually a black box, will be useful. In this study, we analyze the explainability of the machine learning model estimating charge states in quantum dots by gradient-weighted class activation mapping, which identified class-discriminative regions for the predictions. The model predicts the state based on the change transition lines, indicating that human-like recognition is realized. We also demonstrate improvements of the model by utilizing feedback from the mapping results. Due to the simplicity of our simulation and pre-processing methods, our approach offers scalability without significant additional simulation costs, demonstrating its suitability for future quantum dot system expansions.

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arXiv2020

Gate voltage dependence of noise distribution in radio-frequency reflectometry in gallium arsenide quantum dots

We investigate gate voltage dependence of electrical readout noise in high-speed rf reflectometry using gallium arsenide quantum dots. The fast Fourier transform spectrum from the real time measurement reflects build-in device noise and circuit noise including the resonator and the amplifier. We separate their noise spectral components by model analysis. Detail of gate voltage dependence of the flicker noise is investigated and compared to the charge sensor sensitivity. We point out that the dominant component of the readout noise changes by the measurement integration time.

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