Automatic tuning of a donor in a silicon quantum device using machine learning

TL;DR

This paper introduces a machine learning algorithm that automatically locates charge transitions, tunes charge readout, and balances tunnelling rates in silicon donor qubits, significantly speeding up device characterization and tuning.

Contribution

It presents the first ML-based method for automatic tuning of silicon donor qubits, reducing tuning time from hours to minutes.

Findings

Automatic charge transition detection achieved within minutes.

Tuning of charge readout performed without human intervention.

Balanced tunnelling rates identified efficiently.

Abstract

Donor spin qubits in silicon offer one- and two-qubit gates with fidelities beyond 99%, coherence times exceeding 30 seconds, and compatibility with industrial manufacturing methods. This motivates the development of large-scale quantum processors using this platform, and the ability to automatically tune and operate such complex devices. In this work, we present the first machine learning algorithm with the ability to automatically locate the charge transitions of an ion-implanted donor in a silicon device, tune single-shot charge readout, and identify the gate voltage parameters where tunnelling rates in and out the donor site are the same. The entire tuning pipeline is completed on the order of minutes. Our results enable both automatic characterisation and tuning of a donor in silicon devices faster than human experts.