Efficient quantum device tuning using machine learning

Thesis

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.

Authors: Lennon, DT

• DOI: 10.5287/ora-5z81xqpxe
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: spin qubit quantum computing transport machine learning automation
• Subjects: Physics; Automation; Materials; Computer programming