Efficiently tuning quantum devices using machine learning

Controlling materials on the nanoscale is now sufficiently refined that new methods are needed for fabricating the structures and tuning their performance. In many cases we are reaching the limits of our ability to do this using human control of the process, all the more so when a key consideration is scalability for technological applications. The project identifies quantum devices where machine learning will provide the key to speeding up, scaling up, and opening up new technologies.

The objective is to optimise the tuning of quantum devices. Quantum device variability is a challenge to tackle, all the more so when a key consideration is scalability for technological applications such as quantum computing. For this, the student will develop machine learning techniques such as deep learning and Bayesian optimisation. Human expert takes hours to find the optimal device parameters, impeding the realisation of large quantum circuits. The parameter space of a single quantum device is at least ten dimensional. To explore this large parameter space, algorithms that allow for an efficient search of optimal parameters will be established. The experiments will be realized at cryogenic temperatures with dedicated low-noise electronics. The quantum devices will be nanostructures defined electrostatically in semiconductors such as gallium arsenide.

Although machine learning is well established for data mining in materials science, and is starting to be used for materials design, the full potential of machine learning for controlling nanoscale experiments is untapped, and there is a clear need for advanced machine learning approaches. The use of machine learning techniques to advance device physics, is in its early stages and it is already proving essential in the fabrication, characterisation and tuning of quantum devices.

This project is key to unleash the potential of quantum technologies by allowing fast measurement of quantum devices, and thus the possibility to control technologically relevant quantum circuits.

A collaboration with University of Basel provides us with the quantum devices we require for our experiments, and a collaboration with the Department of Engineering at University of Oxford allows us to benefit from the expertise of computer scientist dedicated to the study of artificial intelligence, in particular Bayesian optimisation and other machine learning techniques which do not require large amounts of data, which is not available for quantum devices.

This project falls within the EPSRC Quantum technologies research area.

Student is case conversion with company Graphcore