Efficient quantum device tuning using machine learning - Lennon, DT (2021)
Classical computation is foundational to the digital world in which we find ourselves. Quantum computation promises to overhaul the landscape of computation and with it many benefits to society as a whole are predicted. This thesis focuses on spin qubits, devices that hold promise for quantum computation, but are hampered by their control challenges. A large time sink is faced by those in industry and research alike; signals must be acquired, processed, and interpreted. From these signals decisions must be made. This thesis identifies a set of general automated methods to control and interpret devices. All methods are verified through extensive experimental demonstrations proving their efficacy in the real world.