A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/249607 |
Resumo: | The inverse problem of estimating the background potential from measurements of the local density of states is a challenging issue in quantum mechanics. Even more difficult is to do this estimation using approximate methods such as scanning gate microscopy (SGM). Here, we propose a machine-learning-based solution by exploiting adaptive cellular neural networks (CNNs). In the paradigmatic setting of a quantum point contact, the training data consist of potential-SGM functional relations represented by image pairs. These are generated by the recursive Green’s function method. We demonstrate that the CNN-based machine learning framework can predict the background potential corresponding to the experimental image data. This is confirmed by analyzing the estimated potential with image processing techniques based on the comparison between the charge densities and those obtained using different techniques. Correlation analysis of the images suggests the possibility of estimating different contributions to the background potential. In particular, our results indicate that both charge puddles and fixed impurities contribute to the spatial patterns found in the SGM data. Our work represents a timely contribution to the rapidly evolving field of exploiting machine learning to solve difficult problems in physics. |
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Cunha, Carlo Requiao daAoki, NobuyukiFerry, David K.Lai, Ying-Cheng2022-10-03T04:48:57Z20222632-2153http://hdl.handle.net/10183/249607001145760The inverse problem of estimating the background potential from measurements of the local density of states is a challenging issue in quantum mechanics. Even more difficult is to do this estimation using approximate methods such as scanning gate microscopy (SGM). Here, we propose a machine-learning-based solution by exploiting adaptive cellular neural networks (CNNs). In the paradigmatic setting of a quantum point contact, the training data consist of potential-SGM functional relations represented by image pairs. These are generated by the recursive Green’s function method. We demonstrate that the CNN-based machine learning framework can predict the background potential corresponding to the experimental image data. This is confirmed by analyzing the estimated potential with image processing techniques based on the comparison between the charge densities and those obtained using different techniques. Correlation analysis of the images suggests the possibility of estimating different contributions to the background potential. In particular, our results indicate that both charge puddles and fixed impurities contribute to the spatial patterns found in the SGM data. Our work represents a timely contribution to the rapidly evolving field of exploiting machine learning to solve difficult problems in physics.application/pdfengMachine Learning: science and technology. London. Vol. 3, no. 2 (June 2022), 025013, 12 p.Redes neuraisAprendizado de máquinaPontos quânticosCellular neural networksScanning gate microscopyQuantum point contactsA method for finding the background potential of quantum devices from scanning gate microscopy data using machine learningEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001145760.pdf.txt001145760.pdf.txtExtracted Texttext/plain36421http://www.lume.ufrgs.br/bitstream/10183/249607/2/001145760.pdf.txtdc33745271857163e967fbbf0f2ac079MD52ORIGINAL001145760.pdfTexto completo (inglês)application/pdf1268070http://www.lume.ufrgs.br/bitstream/10183/249607/1/001145760.pdf2ea6c2498be7ebb7c2170fb638455816MD5110183/2496072023-07-08 03:35:23.129298oai:www.lume.ufrgs.br:10183/249607Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-08T06:35:23Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
title |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
spellingShingle |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning Cunha, Carlo Requiao da Redes neurais Aprendizado de máquina Pontos quânticos Cellular neural networks Scanning gate microscopy Quantum point contacts |
title_short |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
title_full |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
title_fullStr |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
title_full_unstemmed |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
title_sort |
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning |
author |
Cunha, Carlo Requiao da |
author_facet |
Cunha, Carlo Requiao da Aoki, Nobuyuki Ferry, David K. Lai, Ying-Cheng |
author_role |
author |
author2 |
Aoki, Nobuyuki Ferry, David K. Lai, Ying-Cheng |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Cunha, Carlo Requiao da Aoki, Nobuyuki Ferry, David K. Lai, Ying-Cheng |
dc.subject.por.fl_str_mv |
Redes neurais Aprendizado de máquina Pontos quânticos |
topic |
Redes neurais Aprendizado de máquina Pontos quânticos Cellular neural networks Scanning gate microscopy Quantum point contacts |
dc.subject.eng.fl_str_mv |
Cellular neural networks Scanning gate microscopy Quantum point contacts |
description |
The inverse problem of estimating the background potential from measurements of the local density of states is a challenging issue in quantum mechanics. Even more difficult is to do this estimation using approximate methods such as scanning gate microscopy (SGM). Here, we propose a machine-learning-based solution by exploiting adaptive cellular neural networks (CNNs). In the paradigmatic setting of a quantum point contact, the training data consist of potential-SGM functional relations represented by image pairs. These are generated by the recursive Green’s function method. We demonstrate that the CNN-based machine learning framework can predict the background potential corresponding to the experimental image data. This is confirmed by analyzing the estimated potential with image processing techniques based on the comparison between the charge densities and those obtained using different techniques. Correlation analysis of the images suggests the possibility of estimating different contributions to the background potential. In particular, our results indicate that both charge puddles and fixed impurities contribute to the spatial patterns found in the SGM data. Our work represents a timely contribution to the rapidly evolving field of exploiting machine learning to solve difficult problems in physics. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-10-03T04:48:57Z |
dc.date.issued.fl_str_mv |
2022 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/249607 |
dc.identifier.issn.pt_BR.fl_str_mv |
2632-2153 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001145760 |
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2632-2153 001145760 |
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http://hdl.handle.net/10183/249607 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Machine Learning: science and technology. London. Vol. 3, no. 2 (June 2022), 025013, 12 p. |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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