Machine learning for the study and optimisation of spin qubits in silicon quantum dots

This PhD project focuses on the application of a wide range of Machine Learning techniques to advance the characterization and measurement of Silicon CMOS quantum devices. With the aim to explore and gain a comprehensive understanding of diverse ML methodologies, including Bayesian analysis, variational algorithms, and reinforcement learning, this projects primary objective is to develop robust models that can effectively minimize the time and effort invested by researchers in characterizing and measuring each device. By implementing efficient fast readout techniques of Silicon quantum dots, the project aims to streamline the process of measurement and characterisation of quantum devices, leading to enhanced efficiency, accuracy, and reliability of results.