Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams

TL;DR

This paper develops and compares automated methods for detecting charge transition edges in quantum dot stability diagrams, aiming to improve qubit tuning automation using simulated and experimental data.

Contribution

It introduces and evaluates automated detection techniques trained on simulated data for reliable charge transition identification in quantum dot diagrams.

Findings

Optimized detection methods perform well on simulated data.

Methods show promising results on experimental GaAs and SiGe qubit data.

Quantitative comparison guides future hardware implementation.

Abstract

Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability diagrams, in which charge transitions manifest as edges. Therefore, to fully automate qubit tuning, it is necessary to recognize these edges automatically and reliably. This paper investigates possible detection methods, describes their training with simulated data from the SimCATS framework, and performs a quantitative comparison with a future hardware implementation in mind. Furthermore, we investigated the quality of the optimized approaches on experimentally measured data from a GaAs and a SiGe qubit sample.