Noise-robust classification of single-shot electron spin readouts using a deep neural network

Abstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise...

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Autores principales:	Yuta Matsumoto, Takafumi Fujita, Arne Ludwig, Andreas D. Wieck, Kazunori Komatani, Akira Oiwa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e073c3ef709c42d6bdcb1d1cbc4dd41d
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spelling	oai:doaj.org-article:e073c3ef709c42d6bdcb1d1cbc4dd41d2021-12-02T14:58:45ZNoise-robust classification of single-shot electron spin readouts using a deep neural network10.1038/s41534-021-00470-72056-6387https://doaj.org/article/e073c3ef709c42d6bdcb1d1cbc4dd41d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00470-7https://doaj.org/toc/2056-6387Abstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature, and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down traces in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments.Yuta MatsumotoTakafumi FujitaArne LudwigAndreas D. WieckKazunori KomataniAkira OiwaNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa Noise-robust classification of single-shot electron spin readouts using a deep neural network
description	Abstract Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature, and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down traces in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments.
format	article
author	Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa
author_facet	Yuta Matsumoto Takafumi Fujita Arne Ludwig Andreas D. Wieck Kazunori Komatani Akira Oiwa
author_sort	Yuta Matsumoto
title	Noise-robust classification of single-shot electron spin readouts using a deep neural network
title_short	Noise-robust classification of single-shot electron spin readouts using a deep neural network
title_full	Noise-robust classification of single-shot electron spin readouts using a deep neural network
title_fullStr	Noise-robust classification of single-shot electron spin readouts using a deep neural network
title_full_unstemmed	Noise-robust classification of single-shot electron spin readouts using a deep neural network
title_sort	noise-robust classification of single-shot electron spin readouts using a deep neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e073c3ef709c42d6bdcb1d1cbc4dd41d
work_keys_str_mv	AT yutamatsumoto noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT takafumifujita noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT arneludwig noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT andreasdwieck noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT kazunorikomatani noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork AT akiraoiwa noiserobustclassificationofsingleshotelectronspinreadoutsusingadeepneuralnetwork
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Noise-robust classification of single-shot electron spin readouts using a deep neural network

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