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

07/27/2021

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

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

Related Research

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Quantum device fine-tuning using unsupervised embedding learning

Bridging the reality gap in quantum devices with physics-aware machine learning

Efficiently measuring a quantum device using machine learning

Identifying Pauli spin blockade using deep learning

Tuning arrays with rays: Physics-informed tuning of quantum dot charge states

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