Spin qubit tuning using machine learning

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Spin qubits, quantum bits based on electron spins, are promising for the realization of large quantum circuits based on current integrated circuit technologies. To realise the full potential of these qubits, the optimal operation parameters have to be found. We are now reaching the limits of our ability to do this manually. The project might allow for the fully automated search of optimal qubit operation parameters, talking the bottleneck of scalability in quantum technologies.The objective is to achieve fully-automatic optimisation of the operation parameters of a spin qubit. This would enable us to optimise many qubits at once and thus allow for the realization of complex quantum circuits. For this, the student will develop machine learning techniques such as Bayesian optimisation and inference. In this way, we expect that algorithm would be faster and better than human experts at qubit optimisation. The parameter space of a single qubit is multidimensional. To efficiently explore the entire parameter space, algorithms that consider exploitation versus exploration trade-offs will be established. The qubits will be measured at cryogenic temperatures with dedicated low-noise electronics. The qubits will be electrostatically defined in semiconductors, such as silicon and gallium arsenide. Machine learning has been used in recent breakthroughs, such as the victory of an algorithm over a Go world champion and super-human face recognition. There is a clear need for machine learning approaches to unleash the potential of qubit technologies. The full potential of machine learning to control qubit experiments has not yet been explored, while the use of machine learning techniques to advance quantum experiments is already proving essential in the fabrication and characterisation of quantum devices. Our collaborators in IST Austia provides us with the qubits we require, and a collaboration with the Department of Engineering at University of Oxford allows us to have access to the latest techniques in machine learning, in particular Bayesian optimisation and other approaches for which large amounts of data are not available. This project is important to advance quantum technologies by allowing fast and automatic optimisation of qubit parameters, thus enabling the possibility to optimise complex quantum circuits. New machine learning approaches that have become available in the last few years might allow us to tackle this challenge. This project falls within the EPSRC Quantum technologies research area.