Learning Coulomb Diamonds in Large Quantum Dot Arrays

We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation....

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Main Author: Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg
Format: Article
Language:English
Published: SciPost 2022-10-01
Series:SciPost Physics
Online Access:https://scipost.org/SciPostPhys.13.4.084
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author Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg
author_facet Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg
author_sort Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg
collection DOAJ
description We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.
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spelling doaj.art-436b9c7278be45768b8fb984896407542022-12-22T02:25:06ZengSciPostSciPost Physics2542-46532022-10-0113408410.21468/SciPostPhys.13.4.084Learning Coulomb Diamonds in Large Quantum Dot ArraysOswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van NieuwenburgWe introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.https://scipost.org/SciPostPhys.13.4.084
spellingShingle Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg
Learning Coulomb Diamonds in Large Quantum Dot Arrays
SciPost Physics
title Learning Coulomb Diamonds in Large Quantum Dot Arrays
title_full Learning Coulomb Diamonds in Large Quantum Dot Arrays
title_fullStr Learning Coulomb Diamonds in Large Quantum Dot Arrays
title_full_unstemmed Learning Coulomb Diamonds in Large Quantum Dot Arrays
title_short Learning Coulomb Diamonds in Large Quantum Dot Arrays
title_sort learning coulomb diamonds in large quantum dot arrays
url https://scipost.org/SciPostPhys.13.4.084
work_keys_str_mv AT oswinkrauseanasuachatterjeeferdinandkuemmethevertvannieuwenburg learningcoulombdiamondsinlargequantumdotarrays