It might be hard to imagine, but despite the central role of physics in quantum computing, quantum computing techniques have not really played a role in solving physics research problems. That is, at least, not until now.
Researchers from Caltech and the University of Southern California (USC) have employed quantum-compatible machine learning techniques to extract a rare Higgs boson signal from copious noise data. Higgs, as you may recall from the announcement of its discovery at the Large Hadron Collider in 2012, is the particle that was predicted to imbue elementary particles with mass.
In the new work, the researchers programmed a quantum annealer – a type of quantum computer designed to run optimization tasks -- to sort through particle-measurement data filled with errors. By seeking patterns within a dataset, the program was designed to differentiate meaningful data from junk. They found that it performed well even with small datasets, unlike the standard counterparts.
The accuracy of classification through prior techniques depends strongly on the size and quality of a manually sorted portion of the dataset, known as the training set. This is problematic for high-energy physics research, however, which revolves around rare events buried in large amounts of noise data. By contrast, the new quantum program “is simpler, takes very little training data, and could even be faster,” according to Professor Maria Spiropulu of Caltech.
"The Large Hadron Collider generates a huge number of events, and the particle physicists have to look at small packets of data to figure out which are interesting," said Joshua Job, a physics graduate student who is co-author of a paper on the research published in the journal Nature.
Still, modeling the problem in a way that the quantum annealer could understand was a challenge.
"Programming quantum computers is fundamentally different from programming classical computers,” said Alex Mott (PhD 15), a former graduate student of Spiropulu. “It's like coding bits directly. The entire problem has to be encoded at once, and then it runs just once as programmed.”
The researchers are actively seeking further applications of the new quantum-annealing classification technique, including computational biology, particle-tracking and examination of charged particles.
"The result of this work is a physics-based approach to machine learning that could benefit a broad spectrum of science and other applications," said Spiropulu.