Machine Learning for Quantum Experiments

In this talk I aim to showcase how machine learning inspired optimisations can help with current state-of-the-art experiments. In particular, I will first consider the readout of semiconductor spin qubits using simple principal component analysis. I will then highlight a specifically fabricated semiconductor device with a 3x3 ‘pixel array’, and discuss the simultaneous tuning of those 9 gate voltages to construct a quantum point contact. And finally, I will move on to larger arrays of quantum dots and the detection of transitions between charge states (i.e. finding the facets of high-dimensional Coulomb diamonds).