Automated tuning of double quantum dots into specific charge states using neural networks

TL;DR

This paper presents a machine learning algorithm that automates the tuning of double quantum dots into specific charge states, significantly reducing manual effort and improving efficiency in quantum device calibration.

Contribution

It introduces a novel neural network-based method for automated tuning of quantum dots using minimal measurements, advancing quantum device control techniques.

Findings

Algorithm reliably reaches target charge states in experiments.

Reduces tuning time compared to manual methods.

Demonstrated on GaAs double quantum dot device.

Abstract

While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and difficult to perform manually as the devices become more complex and the number of tuning parameters grows. In this work, we present a crucial step towards automated tuning of quantum dot qubits. We introduce an algorithm driven by machine learning that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state. We train and test our algorithm on a GaAs double quantum dot device and we consistently arrive at the desired state or its immediate neighborhood.