Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Abstract

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

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• Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams
• Robust quantum dots charge autotuning using neural network uncertainty
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
• Bootstrapping, autonomous testing, and initialization system for Si/Si${}_x$Ge${}_{1-x}$ multi-quantum-dot devices