arXiv:2211.04504

All rf-based tuning algorithm for quantum devices using machine learning

Barnaby van Straaten, Federico Fedele, Florian Vigneau, Joseph Hickie, Daniel Jirovec, Andrea Ballabio, Daniel Chrastina, Giovanni Isella, Georgios Katsaros, Natalia Ares

Published Nov 8, 2022• 2 citations

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Abstract

Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.

DOI: 10.48550/arxiv.2211.04504

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Reaching New Heights Together!Congratulations to the group of Natalia Ares at the University of Oxford and their collaborators on demonstrating an all-RF reflectometry quantum device tuning using a machine learning algorithm.The algorithm can tune a double quantum dot in just a few minutes without prior knowledge about the device configuration.This achievement paves the way to more scalable quantum device architectures.We are excited to help the whole team drive the field forward with innovative approaches to quantum measurements using Zurich Instruments lock-in amplifiers.

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