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Data needs and challenges for quantum dot devices automation

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

TL;DR

Current challenges in automating quantum dot device tuning and operation are outlined with a particular focus on datasets, benchmarking, and standardization to provide guidance and inspiration to researchers invested in automation efforts.

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.

Data needs and challenges for quantum dot devices automation

TL;DR

Current challenges in automating quantum dot device tuning and operation are outlined with a particular focus on datasets, benchmarking, and standardization to provide guidance and inspiration to researchers invested in automation efforts.

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.
Paper Structure (7 sections, 1 figure)

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Background and motivation
  2. Automation challenges
  3. Science and process challenges
  4. Guidelines for sharing data
  5. Performance metrics
  6. Testbeds and collaborations
  7. Governance and licensing

Figures (1)

  • Figure 1: Workshop thematic map. The workshop discussions revolved around five main themes: science and policy challenges; guidelines for sharing data; developing performance metrics; establishment of testbed and collaborations; and governance and licensing. These themes are connected to allow the flow of data and information. The end result is an infrastructure for facilitating the development, benchmarking, and standardization of the quantum dot automation methods, where the science is the driver (top row), the testbeds and collaborations are critical (bottom row), and the governance (left box) and the guidelines (right box) take their inputs from the science and provide useful help to the testbed and collaborations. For example, theory, computation, and experiment provide a description of needs that can be met through testbeds and collaboration, the latter two of which are facilitated by governance and guidelines.