Data needs and challenges for quantum dot devices automation

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

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Publication Details

Journal
npj Quantum Information
Published
2024-10-31
DOI
https://doi.org/10.1038/s41534-024-00878-x
Citations
9
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
3.13

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article

Data needs and challenges for quantum dot devices automation

Michael J. Gullans, Mick Ramsey, Donovan Buterakos, Anasua Chatterjee et al.
9 citations
npj Quantum Information
Quantum Computing Algorithms and Architecture
3.13
article

Data needs and challenges for quantum dot devices automation

Michael J. Gullans, Mick Ramsey, Donovan Buterakos, Anasua Chatterjee, Pranav Mundada, Brian Weber, A. J. Sigillito, S. Das Sarma, Eliška Greplová, Brennan Undseth, Garnett W. Bryant, Torbjørn Rasmussen, Justyna P. Zwolak, Fabian Hader, Brandon Severin, M. A. Eriksson, Tyler J. Kovach, Jacob M. Taylor, Reed W. Andrews, J D Benson
article en
9 citations

Abstract

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

npj Quantum InformationVol. 10(1)
University of Copenhagen (DK), National Institute of Standards and Technology (US), Intel (United States) (US), University of Wisconsin–Madison (US), Forschungszentrum Jülich (DE), HRL Laboratories (United States) (US), UNSW Sydney (AU), University of Oxford (GB), University of Santa Monica (US), Joint Center for Quantum Information and Computer Science (US), Yukhnovskii Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine (UA), QuTech (NL), Joint Quantum Institute (US), Intelligent Health (United Kingdom) (GB), University of Maryland, College Park (US), University of Pennsylvania (US), Delft University of Technology (NL)
National Institute of Standards and Technology
Openalex Percentile: Top 7%
Quantum Computing Algorithms and Architecture
3.13
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