Figure files for "Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays" submitted to Physical Review X

The dataset underlying the figures in the manuscript is "Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays."Abstract of the paper: Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.Data description: Each figure folder contains a complete set of files necessary to reproduce figures, including Jupyter Notebooks with the figure source code, Adobe Illustrator, and pre-processed data files (hdf5 and pkl). The complete set of all raw data files used in this study is available at Zenodo. [doi: 10.5281/zenodo.14173838].Acknowledgments: This research was sponsored in part by the Army Research Office (ARO) under Awards No. W911NF-23-1-0110 and W911NF-23-1-0258. We acknowledge support from the European Union through the IGNITE project with grant agreement No. 101069515 and from the Dutch Research Council (NWO) via the National Growth Fund program Quantum Delta NL (Grant No. NGF.1582.22.001). The views, conclusions, and recommendations contained in this paper are those of the authors and are not necessarily endorsed nor should they be interpreted as representing the official policies, either expressed or implied, of the Army Research Office (ARO) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.

Tags: 2d-arrays, autonomous-control, germanium-quantum-dots, machine-learning, quantum-dots
Organization: National Institute of Standards and Technology