Speaker
Description
Machine learning is rapidly proving indispensable in tuning and characterising quantum devices. By facilitating the exploration of high-dimensional and complex parameter spaces, these algorithms not only allow for the identification of optimal operational conditions but also surpass human experts in the characterisation of different operational regimes. I will present the first fully autonomous tuning of a spin qubit. This is a major advancement for scaling semiconductor quantum technologies and understanding variability in nominally identical devices. My discussion will also cover the versatility of machine learning algorithms across various semiconductor devices, emphasising their role in the comparative analysis of quantum device architectures. I will demonstrate how a physics-informed machine learning approach can reveal the disorder potential in a quantum dot device, providing insights into device characteristics that were previously inaccessible. I will conclude by discussing how machine learning can bridge the gap between quantum device simulation and reality, catalysing rapid advancements in quantum technology.
