



@article {3396,
	title = {Data Needs and Challenges of Quantum Dot Devices Automation: Workshop Report},
	year = {2023},
	month = {12/21/2023},
	abstract = {<p>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.</p>},
	doi = {https://doi.org/10.48550/arXiv.2312.14322},
	url = {https://arxiv.org/abs/2312.14322},
	author = {Justyna P. Zwolak and Jacob M. Taylor and Reed Andrews and Jared Benson and Garnett Bryant and Donovan Buterakos and Anasua Chatterjee and Sankar Das Sarma and Mark A. Eriksson and Eli{\v s}ka Greplov{\'a} and Michael Gullans and Fabian Hader and Tyler J. Kovach and Pranav S. Mundada and Mick Ramsey and Torbjoern Rasmussen and Brandon Severin and Anthony Sigillito and Brennan Undseth and Brian Weber}
}
@article {3079,
	title = {Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research},
	year = {2022},
	month = {05/17/2022},
	abstract = {<p>We establish a dataset of over 1.6\&times;104 experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 \% of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.</p>},
	url = {https://arxiv.org/abs/2205.09114},
	author = {Amilson R. Fritsch and Shangjie Guo and Sophia M. Koh and I. B. Spielman and Justyna P. Zwolak}
}
