Machine learning enables completely automatic tuning of a quantum device faster than human experts
Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explored in order to find the optimal operating conditions. We demonstrate a statistical tuning algorithm that navigates this...
Main Authors: | , , , , , , , , , , , , |
---|---|
Format: | Journal article |
Language: | English |
Published: |
Springer Nature
2020
|