One of the near-term applications of quantum computing is for machine learning. For example, the D-wave chip is for optimising a binary fitness function. We will soon see if it works out.
To be fair, the price for that RAM starts to steepen at (if not before) the 4TB mark, and 1.5TB might have been the limit on frugal main memory as recently as a year and a half ago.
OTOH, SSDs are very fast even at low cost, so I'd argue if it can fit in directly-attached storage whose aggregate bandwidth compares with the RAM's bandwidth, it ain't Big Data, either. Though even a single PB might be big enough, if the CPUs are too slow.
So the logic goes like this: if we trust our simulation, we can simulate the Higgs, and simulate the background, and then train a neural network to tell us which is which. Then we turn the network loose on our data. If it sees lots of things that look like Higgs, yay, we discovered something!
For machine learning tools, we had a few homegrown implementations that didn't get far beyond physics (probably because they weren't particularly user-friendly). But physicists would have referred to techniques like this as "Multivariate Analysis" (or "MVA") a few years ago.
More recently we've started to reach out more to industry and use their tools, which actually much nicer! What she's referring to here is one particular analysis her team contributed to [1], which relied on XGboost [2]. Beyond that we've used Keras a fair bit to identify some types of particles.
[1]: https://arxiv.org/abs/1806.00425 [2]: https://xgboost.readthedocs.io/en/latest/
In genereal it is wise to know that Machine Learning, Big Data and Cloud computing has been used in particle physics for decades, but with the LHC a world-wide infrastructure has been created to capture all the learnings that the mainstream are only beginning to discover now. For instance a main paradigm in the analysis model is to move calculation to the data, rather than the other way around, due to the large amounts of data. You may call it MapReduce, we call it physics analysis (Map you statistical analysis across decentralised data, reduce the output through distributed merge jobs, plot and publish). Sorry to sound like an old fart, you question is honest and relevant, but it really underlines how easy a story about how Google/Facebook/whatever invented something can rewrite history. Most of the stuff people in the IT/Tech sector are playing around with are inspired by basic or applied science, and applied in a commercial setting. This is exactly how it is supposed to work, but damned if the log analysing marketeers at Google should have all the credit for these developments :)
Now, with my rant over, here are a few references that may be interesting to you:
These were the tools used for physics analysis:
http://tmva.sourceforge.net https://root.cern.ch https://twiki.cern.ch/twiki/bin/view/RooStats/WebHome
And a few articles http://atlas.cern/search/node/Boosted%20Decision%20tree https://cds.cern.ch/search?ln=en&sc=1&p=Machine+Learning&act...
Oh and a bit of gossip. We called Sau Lan Wu the "Dragon lady" (mostly behind her back), because of her awesome energy and tenacity. She really deserves the credit given in the article!
I think the blame here is mostly on the science community which isn't paying much attention to ML tooling, best practices and research and instead keeps reinventing the wheel, over and over again.