English

Data needs and challenges for quantum dot devices automation

Mesoscale and Nanoscale Physics 2024-11-07 v3 Databases Machine Learning Quantum Physics

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.

Keywords

Cite

@article{arxiv.2312.14322,
  title  = {Data needs and challenges for quantum dot devices automation},
  author = {Justyna P. Zwolak and Jacob M. Taylor and Reed W. Andrews and Jared Benson and Garnett W. Bryant and Donovan Buterakos and Anasua Chatterjee and Sankar Das Sarma and Mark A. Eriksson and Eliška Greplová and Michael J. Gullans and Fabian Hader and Tyler J. Kovach and Pranav S. Mundada and Mick Ramsey and Torbjørn Rasmussen and Brandon Severin and Anthony Sigillito and Brennan Undseth and Brian Weber},
  journal= {arXiv preprint arXiv:2312.14322},
  year   = {2024}
}

Comments

A meeting report from a workshop held at the National Institute of Standards and Technology, Gaithersburg, MD

R2 v1 2026-06-28T13:59:20.498Z