Ray-based automatic tuning of a single quantum dot in a GaAs/AlGaAs quadruple dot array

Abstract Semiconductor quantum dot arrays, a key platform for spin-based quantum computing, face scalability challenges due to the time-consuming manual tuning of potential landscapes. This paper proposes that combining minimal one-dimensional voltage sweeps with machine learning offers a practical method for automating the tuning of four separate single quantum dots, inspired by the Ray-based Classification framework. Our method successfully tunes single quantum dots in a GaAs quadruple quantum dot device, while reducing the complexity of data acquisition and simplifying the tuning process.