Efficient tuning of 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 develop theoretical models that can account for transport in semiconductor quantum devices to inform machine learning techniques which will allow for fast tuning and measurement of these devices. These techniques will be focused towards measurement algorithms which prioritize information gain, determining which is the most informative measurement to perform next. The algorithm will combine information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. An optimised measurement method that can prioritise and select important configurations is key for fast characterisation and automatic tuning. A physical model will aid the training of the algorithms to prevent over-fitting. This project will involve statistical problem-framing, to identify what is to be predicted as a function of what, and it will require to select and evaluate different models of quantum devices.

Most machine learning techniques dedicated to the tuning and measurement of quantum devices so far have no knowledge about the physics behind the operation of these devices. This project is dedicated to produce adequate physics models which can inform the machine learning methods in the efficient measurement 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.