Silicon quantum dots (QDs) are a compelling platform for a fault-tolerant quantum computer. However, due to inter-dot coupling, it is difficult to control each QD independently, resulting in a complex calibration process, which becomes impossible to heuristically tune as the number of qubits increases towards a NISQ device. My research entailed developing an automated tuning algorithm that allows to control each QD independently within a 2xN array by leveraging different data processing and machine learning algorithms. 

PhD Supervisors: Prof Charles Smith – Department of Physics, Alpha Lee – Department of Physics and Miguel Fernando Gonzalez-Zalba – QMT (Industrial)

Research Topic: Automating quantum computers using ML