Quantum Machine Learning promising in CERN’s LHCb experiment

30 July 2022

A quantum-flavoured milestone in high energy physics: the LHCb collaboration at CERN reports its first quantum computing-related work. In a publication in the Journal of High Energy Physics, they describe the first application of Quantum Machine Learning for identifying properties of streams of particles that result from b-quarks in high-energy 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.

Dizzying numbers

The LHCb experiment is one of the experiments at CERN’s Large Hadron Collider, partly built and run by Nikhef. 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.

Machine learning

The authors now investigate the use of Quantum Machine Learning – a new iteration of the field, which employs principles from quantum computing – to tag jets containing particles that are of particular interest to the LHCb experiment. Using simulated datasets, the researchers compare the performance of more classical approaches to that of quantum algorithms.

In some scenarios, these performed comparable to their conventional counterparts. As such, this is a promising first exploration of what quantum algorithms could do for big physics experiments.

Maastricht and Nikhef

This work represents the first application of quantum computing to the identification of jets that originate from beauty quarks or anti-quarks. The work was performed at the University of Padua in Italy by the LHCb Data Processing & Analysis Group, in which Maastricht University takes part. Co-corresponding author Davide Nicotra has since joined Maastricht University as a PhD student, where he will continue to work on quantum computing applications for LHCb particle tracking challenges.