[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121884-en":3,"doc-seo-121884-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121884,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Fast characterization of multiplexed single-electron pumps with machine learning - automated framework","An efficient machine-learning based automated framework enables fast tuning of single-electron pump devices into current quantization regimes using sparse, targeted measurements. An iterative active learning algorithm selects informative gate-voltage points in the control-parameter space, reducing measurement counts by about an order of magnitude versus conventional scans. This yields an eight-fold decrease in time to determine quantization errors, estimated via exponential extrapolation from the first current plateau boundary. The method robustly characterizes 28 GaAs/AlGaAs multiplexer devices and identifies subsets suitable for parallel operation at shared gate voltages, supporting scalable characterization of many pumps.","arXiv :2405 .20946v1 [ cond-mat .mes-hall ] 31 May 2024  \nFast characterization of multiplexed single-electron pumps with machine learning  \nN. Schoinas,1, a) Y. Rath,1, a) S. Norimoto,1 W. Xie,1 P. See,1 J. P. Griffiths,2 C. Chen,2 D. A. Ritchie,2 M.  \nKataoka,1 A. Rossi,1, 3, b) and I. Rungger1, 4, c)  \n1) National Physical Laboratory, Teddington, TW11 0LW, United Kingdom.  \n2) Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom  \n3) Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom  \n4) Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX, United Kingdom.  \n(Dated: 3 June 2024)  \nWe present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measurement approach based on an iterative active learning algorithm to take targeted measurements in the gate voltage parameter space. When compared to conventional parameter scans, our automated framework allows us to decrease the number of measurement points by about an order of magnitude. This corresponds to an eight-fold decrease in the time required to determine quantization errors, which are estimated via an exponential extrapolation of the first current plateau embedded into the algorithm. We show the robustness of the framework by characterizing 28 individual devices arranged in a GaAs/AlGaAs multiplexer array, which we use to identify a subset of devices suitable for parallel operation at communal gate voltages. The method opens up the possibility to efficiently scale the characterization of such multiplexed devices to a large number of pumps.  \nSingle-electron pumps are nanoscale devices that can produce quantized macroscopic electric currents by clocking the transfer of individual electrons to an external periodic drive 1–9. This device technology has been primarily developed to realize the practical implementation of the SI unit of current, the ampere, which since 2019 is defined by the fixed value of the elementary charge, e 10–12. The overarching goal is the experimental realization of devices generating a quantized current according to the relationship I = ne f, where f is the periodic drive frequency and n is an integer multiple of electrons transferred in a cycle.  \nThe device operation requires a large degree of manual intervention to find the appropriate operation conditions in a large space of control parameters. With the increasing need of operating multiplexed devices in a parallel configuration to generate usefully large quantized currents 13–15 , the manual tuning of control parameters for each device becomes a significant bottleneck. Since each pump has slightly different operating parameters, this severely limits the pace at which candidate devices can be screened.  \nTo tackle this limitation, here we present a machine learning (ML) based framework that supports the automatic tuning of multiple single-electron pumps. The use of ML for experimental control of quantum devices is becoming increasingly popular, as it may unleash significant speedups 16,17 . Our framework automatically finds and  \na) These two authors contributed equally  \nb) [alessandro.rossi@npl.co.uk](alessandro.rossi@npl.co.uk)  \nc) [ivan.rungger@npl.co.uk](ivan.rungger@npl.co.uk)  \ncharacterizes the n = 1 plateau in single-electron pumps as a function of control DC voltages. We focus on then = 1 plateau since this is the region where the pumps are typically operated to achieve the best current quantization4,18 . We present an active learning (AL) sparse measurement (ALSM) framework in which measurements are obtained iteratively in a data-driven approach, which is designed to gain the necessary information from as few measurements as possible. To this aim, the method needs to find the boundaries of the n = 1 plateau and acquire sufficient data to perform an exponential ","cbCaihbXHHAURkll","https://ap.wps.com/l/cbCaihbXHHAURkll","pdf",870329,1,6,"English","en",105,"# Overview\n# Active learning sparse measurement framework\n# Device definition and quantization error\n# Application to GaAs/AlGaAs multiplexers\n# Scalability and parallel operation selection","[{\"question\":\"How does the framework speed up tuning of single-electron pumps?\",\"answer\":\"It uses an iterative active learning sparse measurement strategy to take targeted measurements in gate-voltage space, cutting the number of measurement points by about an order of magnitude compared with conventional parameter scans.\"},{\"question\":\"How are quantization errors estimated in the method?\",\"answer\":\"Quantization errors are derived by exponential extrapolation from data near the boundary of the n=1 current plateau to its center, enabling estimation beyond the measurement noise floor.\"},{\"question\":\"Why is the focus on the n=1 plateau?\",\"answer\":\"The n=1 plateau corresponds to the operating region where pumps typically achieve the best current quantization, making it the most relevant target for automated tuning.\"}]","Fast characterization of multiplexed single-electron pumps with machine learning - 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