



01798nas a2200133   4500008004100000245007100041210006900112260001400181490000700195520137900202100002401581700002201605856003701627       2023                        eng d00aColloquium:  Advances in automation of quantum dot devices control0 aColloquium Advances in automation of quantum dot devices control  c2/17/20230 v953 a<p>Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.</p>1 aZwolak, Justyna, P.1 aTaylor, Jacob, M.  uhttps://arxiv.org/abs/2112.0936201635nas a2200181   4500008004100000245010500041210006900146260001500215300001200230490000600242520106000248100001801308700002001326700002501346700002101371700002401392856003701416       2022                        eng d00aCombining machine learning with physics: A framework for tracking and sorting multiple dark solitons0 aCombining machine learning with physics A framework for tracking  c06/01/2022  a023163 0 v43 a<p>In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.</p>1 aGuo, Shangjie1 aKoh, Sophia, M.1 aFritsch, Amilson, R.1 aSpielman, I., B.1 aZwolak, Justyna, P.  uhttps://arxiv.org/abs/2111.04881