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

该博士项目致力于应用广泛的机器学习技术来提高硅CMOS型量子器件的表征和测量。为了探索和全面了解不同的ML方法，包括贝叶斯分析、变分算法和强化学习，该项目的主要目标是开发健壮的模型，可以有效地减少研究人员在表征和测量每个设备上投入的时间和精力。通过实施硅量子点的高效快速读出技术，该项目旨在简化量子设备的测量和表征过程，从而提高结果的效率、准确性和可靠性。

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.