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

Barnaby van Straaten1,*, Federico Fedele1,*, Florian Vigneau1, Joseph Hickie1, Daniel Jirovec2, Andrea Ballabio3, Daniel Chrastina3, Giovanni Isella3, Georgios Katsaros2 et al.

Natalia Ares4,†

  • *These authors contributed equally.
  • †Contact author: natalia.ares@eng.ox.ac.uk
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Phys. Rev. Applied 24, 054030 – Published 10 November, 2025

DOI: https://doi.org/10.1103/v11m-dbhm

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, that is, without resorting to current measurements. We demonstrate an algorithm that performs automatic coarse tuning of double quantum dots with only radio-frequency measurements by exploiting their bandwidth and impedance matching. The tuning was completed within a few minutes with minimal prior knowledge about the device. 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.

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Phys. Rev. Applied 24, 054030– Published 10 November, 2025
  • Received 26 September 2024
  • Revised 23 June 2025
  • Accepted 26 September 2025
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