Characterisation and fully autonomous tuning of spin qubits with machine learning

Thesis

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real ‘needle in the haystack’ problem, which, until now, has resisted complete automation due to device variability, fabrication imperfections, and limited data availability.

In this thesis, I first present a machine learning algorithm capable of automatically identifying Pauli spin blockade (PSB) using charge transport measurements. PSB can be employed as a resource for spin qubit initialisation and readout but it can be difficult to identify. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. I demonstrate the approach on a silicon field-effect transistor device and report a high accuracy on test devices, giving evidence that the approach is robust to device variability.

Then, I present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. This automation, achieved without human intervention, is demonstrated in a Ge/Si core/shell nanowire device and integrates deep learning, Bayesian optimisation, and computer vision techniques.

Further, I demonstrate the potential of full automation by characterising how the Rabi frequency and g-factor depend on barrier gate voltages for qubits in four different charge transitions. The data reveals that certain features in charge transport measurements can enhance automated tuning processes by recognising the conditions for successful readout.

Finally, I present the autonomous optimisation of two qubits for maximal Rabi frequency. This optimisation extends the tuning algorithm, enabling it to enhance the performance of a qubit.

I hope the mass tuning and characterisation of qubits, enabled by the methods in this thesis, will create a productive feedback loop between measurement and fabrication processes. Wafer-scale, high-throughput characterisation of quantum devices can mitigate device variability and contribute to their scalability. The continued evolution of these methods will support the growing demands of the field, ultimately – and hopefully – leading to practical and powerful quantum computers.

Authors: Schuff, J

• DOI: 10.5287/ora-x5pzq8njm
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: spin qubits; machine learning; quantum computing; automation
• Subjects: Quantum dots; Software; Physics; Quantum computing; Machine learning; Qubits; Automation