Machine learning for quantum device tuning and simulation

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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 combine Bayesian optimisation with other machine learning methods, such as neural networks, to control quantum hardware-in-the-loop systems. Hardware-in-the-loop testing allows actual hardware under test to be interfaced with a software model in real time. Swapping out individual quantum devices one by one, the test results can be used to simulate realistic (and therefore imperfect) signals passing between components of the integrated device. The experiments will be realized at cryogenic temperatures with dedicated radio-frequency electronics. The novelty of the research methodology resides in the use of hardware-in-the-loop simulations to enable the machine to learn tuning techniques in multi quantum device circuits. Our extensible simulation and test platform provides a hardware-in-the-loop facility for applying machine learning to subsystems in a simulated environment, with a view to accelerating the development and prototyping of advanced complex quantum technologies.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.