"Machine learning has returned with a vengeance. I still remember the dark days of the late '80s and '90s, when it was pretty clear that the current generation of machine-learning algorithms didn't seem to actually learn much of anything. Then big data arrived, computers became chess geniuses, conquered Go (twice), and started recommending sentences to judges. In most of these cases, the computer had sucked up vast reams of data and created models based on the correlations in the data.
But this won't work when there aren't vast amounts of data available. It seems that quantum machine learning might provide an advantage here, as a recent paper on searching for Higgs bosons in particle physics data seems to hint." Read the article at Ars Technica.