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.