87 citations · 246 across the 8 of their papers we have counts for
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cond-mat.mes-hall2021★ 14 cited
Bridging the reality gap in quantum devices with physics-aware machine learning
D. L. Craig, H. Moon, F. Fedele +9
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of mater…
cond-mat.mes-hall2021★ 11 cited
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
B. Severin, D. T. Lennon, L. C. Camenzind +20
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 ke…