A team has shown that a quantum circuit can learn to sift through reams of data from atom-smashing experiments in search of a new particle. Their proof-of-principle study — performed using a machine built by quantum-computing company D-Wave working on the now-familiar case of the Higgs boson — does not yet provide a clear advantage over conventional techniques. But the authors say that quantum machine learning could make a difference in future experiments, when the amounts data will grow even larger.
In 2012, two experiments at the Large Hadron Collider (LHC) at CERN, Europe's high-energy physics lab near Geneva, Switzerland, announced that they had proof of the existence of the Higgs boson, the last missing piece in the standard model of particle physics. The two experiments, called CMS and ATLAS, found evidence of the boson created in proton collisions from the way in which the Higgs decayed into more-common ones, such as pairs of high-energy photons. But each time the LHC collides two protons, hundreds of other particles are created, some of which can be misinterpreted as photons when they hit the detectors.