Autonomous tuning and charge state detection of gate defined quantum dots

TL;DR

This paper presents an automated machine learning-based method for tuning and charge state detection of quantum dots in semiconductor devices, reducing manual effort and enabling scalable quantum computing architectures.

Contribution

It introduces a two-stage automated tuning process using supervised machine learning, eliminating manual intervention in quantum dot device characterization.

Findings

Automated tuning achieves reliable quantum dot configuration without manual input.

Supervised classifiers accurately detect charge states and device functionality.

Optimization of models and preprocessing enhances tuning reliability.

Abstract

Defining quantum dots in semiconductor based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum dot regime. We show that automating well established manual tuning procedures and replacing the experimenter's decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without pre-measured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behaviour. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum dot based qubit architectures.