Machine learning for scalable readout of Semiconducting quantum devices

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. Bayesian optimisation is state-of-the-art for expensive (in time or money) problems. Given the available data, Bayesian optimisation methods builds a low-cost machine learning model (an inexpensive probabilistic approximation of the actual experiment) whose predictions are cheap to compute. The model leads to the optimal location for the next measurement. The machine uses the model to find an objective in the most cost-effective way.

The objective is to develop Bayesian optimisation techniques that will allow exquisite control of quantum devices (qubits) in silicon. Different types of silicon quantum devices will be explored: complementary metal-oxide-semiconductor (CMOS), donors in silicon and a new kind of accumulation qubit in silicon. The aim is also to collaborate with internationally leading laboratories in silicon devices, letting the machine control experiments remotely. This will collaboration will increase the capabilities in silicon device control and measurement.

Silicon devices have demonstrated high-fidelity control of electron and nuclear spins and achieved long relaxation and dephasing times, which make them a favourite scalable qubit candidate. No machine learning techniques have yet been applied to the optimisation of these type 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. We will collaborate with UNSW to remotely control their experiments.

This project falls within the EPSRC Quantum technologies research area.