Machine-learning-assisted state and gate engineering for quantum technologies

The interface between these two fields has been the subject of much research, recently, driven by the many successes of machine learning for diverse pattern recognition tasks. The work reported in this Thesis addresses precisely the potential of such an interdisciplinary line of research and illustrates how machine learning provides a valuable add-on to standard techniques used in the context of quantum technologies. <br><br>Specifically, we will first study the problem of devising time-independent dynamics implementing target quantum operations.

This involves a challenging optimisation task, which is hard to tackle with standard numerical tools. We demonstrate how the use of supervised learning algorithms can dramatically speed-up this task. <br><br>We then consider the tasks of engineering and characterising quantum states in large Hilbert spaces. Motivated by strong experimental reasons, we consider explicitly the embodiment of such multi-dimensional quantum systems provided by the orbital angular momentum and polarisation degrees of freedom of light.