Auto-tuning of double dot devices in situ with machine learning

TL;DR

This paper demonstrates an in situ machine learning-based autotuning protocol for quantum dot devices, significantly reducing manual effort and enabling scalable qubit operation by replacing heuristics with trained algorithms.

Contribution

It introduces a machine learning approach trained on simulated data to automate the tuning of double quantum dots in real devices, improving efficiency and scalability.

Findings

Successful active feedback on double-dot devices at millikelvin temperatures

Machine learning classifier effectively replaces human heuristics

Success rate depends on initial conditions and device performance

Abstract

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the {\it in situ} implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.