Robust and fast post-processing of single-shot spin qubit detection events with a neural network

Abstract Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal con...

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Autores principales:	Tom Struck, Javed Lindner, Arne Hollmann, Floyd Schauer, Andreas Schmidbauer, Dominique Bougeard, Lars R. Schreiber
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2be85df74726485ea8884dff9201553d
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spelling	oai:doaj.org-article:2be85df74726485ea8884dff9201553d2021-12-02T16:28:00ZRobust and fast post-processing of single-shot spin qubit detection events with a neural network10.1038/s41598-021-95562-x2045-2322https://doaj.org/article/2be85df74726485ea8884dff9201553d2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95562-xhttps://doaj.org/toc/2045-2322Abstract Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.Tom StruckJaved LindnerArne HollmannFloyd SchauerAndreas SchmidbauerDominique BougeardLars R. SchreiberNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tom Struck Javed Lindner Arne Hollmann Floyd Schauer Andreas Schmidbauer Dominique Bougeard Lars R. Schreiber Robust and fast post-processing of single-shot spin qubit detection events with a neural network
description	Abstract Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.
format	article
author	Tom Struck Javed Lindner Arne Hollmann Floyd Schauer Andreas Schmidbauer Dominique Bougeard Lars R. Schreiber
author_facet	Tom Struck Javed Lindner Arne Hollmann Floyd Schauer Andreas Schmidbauer Dominique Bougeard Lars R. Schreiber
author_sort	Tom Struck
title	Robust and fast post-processing of single-shot spin qubit detection events with a neural network
title_short	Robust and fast post-processing of single-shot spin qubit detection events with a neural network
title_full	Robust and fast post-processing of single-shot spin qubit detection events with a neural network
title_fullStr	Robust and fast post-processing of single-shot spin qubit detection events with a neural network
title_full_unstemmed	Robust and fast post-processing of single-shot spin qubit detection events with a neural network
title_sort	robust and fast post-processing of single-shot spin qubit detection events with a neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2be85df74726485ea8884dff9201553d
work_keys_str_mv	AT tomstruck robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT javedlindner robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT arnehollmann robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT floydschauer robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT andreasschmidbauer robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT dominiquebougeard robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork AT larsrschreiber robustandfastpostprocessingofsingleshotspinqubitdetectioneventswithaneuralnetwork
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Robust and fast post-processing of single-shot spin qubit detection events with a neural network

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