Data Needs and Challenges of Quantum Dot Devices Automation: Workshop
  Report
Justyna P. Zwolak Jacob M. Taylor Reed Andrews Jared Benson Garnett 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 Torbjoern Rasmussen Brandon Severin Anthony Sigillito Brennan Undseth Brian Weber
Gate-defined quantum dots are a promising candidate system to realize
scalable, coupled qubit systems and serve 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. In this report, we outline current
challenges in automating quantum dot device tuning and operation with a
particular focus on datasets, benchmarking, and standardization. We also
present ideas put forward by the quantum dot community on how to overcome them.