Fast tuning, simulation and characterisation of spin qubits devices using machine learning

Thesis

Semiconducting spin quantum devices present a promising avenue for advancing noisy intermediate-scale quantum (NISQ) computers. However, the complexities involved in their tuning and characterisation currently hinder their scaling. This thesis addresses these challenges by harnessing machine-learning-based approaches.

Firstly, I used Gaussian processes to develop a fully automatic tuning algorithm that exclusively used radio-frequency measurements taken on millisecond timescales. Secondly, I employed Bayesian optimisation for charge sensor optimisation and automation. Both algorithms were deployed on depletion mode hole-based Ge/SiGe devices; however, they could easily be applied to other architectures. These automated approaches considerably outperformed manual tuning and represent vital building blocks for algorithms to tune larger quantum dot arrays.

Next, I developed algorithms to find the ground state charge configuration in the constant capacitance model, which was orders of magnitude faster than its predecessors, permitting the simulation of much larger arrays of sixteen dots. This open-source package can be used to gain insights into the charge stability diagrams of large quantum dot arrays and facilitates further tuning algorithm development.

Lastly, I present a novel application of hidden Markov models to identify and separate the state preparation, measurement and spin-flip errors presented by a pair of hot Loss Divincenzo qubits in a Si-MOS device. This approach not only unravels these error sources but also sheds light on their relationship with various parameters, such as electron temperature, thus contributing a vital understanding to the field of quantum computing.

Authors: van Straaten, B

• DOI: 10.5287/ora-y0e2nbeob
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: spin qubits; machine learning; quantum computing