Kavli Affiliate: Menno Veldhorst
| First 5 Authors: Anantha S. Rao, Donovan Buterakos, Barnaby van Straaten, Valentin John, Cécile X. Yu
| Summary:
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.
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