Quantum Machine Learning enters the fray in CERN’s LHCb experiment

In a recent article in the Journal of High Energy Physics (JHEP), the LHCb collaboration reports the application of Quantum Machine Learning for identifying properties of so-called jets: streams of particles that result from particle collisions. It is the first paper to describe the application of quantum computing to the identification of jets originating from beauty quarks or anti-quarks, a type of particle of particular interest to the LHCb experiment. The LHCb experiment is one of the experiments at CERN's Large Hadron Collider. This massive underground research facility replicates conditions shortly after the Big Bang by smashing particles into each other at nearly the speed of light. The resulting collisions hold all sorts of keys to further our understanding of matter and the universe in general. With dizzying numbers of collisions occurring every second, physicists rely on state-of-the-art artificial intelligence to identify the specific particles that interest them. Machine learning has earned its place as a key method in this line of work.