Berkeley lab tackles particle physics with quantum computing

Large-scale particle physics produces large amounts of data as a result – and this is particularly true of the Large Hadron Collider (LHC), the world’s largest particle accelerator, which is housed at the European Organization for Nuclear Research. (CERN) in Switzerland. In 2026, the LHC will benefit from a massive upgrade as part of the High-Luminosity LHC (HL-LHC) project. This will increase the LHC’s data output five to sevenfold – billions of particle events per second – and researchers are scrambling to prepare big data computing for this deluge of particle physics data. Today, researchers at Lawrence Berkeley National Laboratory are working to process large volumes of particle physics data using quantum computing.

When a particle accelerator is running, particle detectors provide data points indicating where particles have crossed certain thresholds in the accelerator. The researchers then try to piece together exactly how the particles traveled through the accelerator, usually using a form of computer-aided pattern recognition.

This project, led by Heather Gray, a professor at the University of California at Berkeley and a particle physicist at the Berkeley laboratory, is called Quantum Pattern Recognition for High-Energy Physics (or HEP.QPR). Essentially, HEP.QPR aims to use quantum computing to speed up this pattern recognition process. HEP.QPR also includes Berkeley Lab scientists Wahid Bhimji, Paolo Calafiura and Wim Lavrijsen.

Their efforts cover a wide range of activities. Bhimji, a big data architect at Berkeley Lab’s National Energy Research Computing Science Center (NERSC), worked to process LHC data with quantum algorithms for associative memory. Elsewhere, project members have worked with Japanese and Canadian researchers to develop quantum algorithms for high-energy physics, including a workshop on the subject in 2019.

Above all, the work of HEP.QPR also includes student researchers. In a recent blog post, Berkeley Lab shed light on the work of several of these researchers. MSc student Lucy Linder wrote her thesis on the application of quantum annealing to find particle traces while working with HEP.QPR; Undergraduate student Eric Rohm developed a Quantum Approximate Optimization Algorithm (QAOA) while participating in the DOE’s Undergraduate Science Lab Internship Program; and Amitabh Yadav, a research associate student at Berkeley Lab, is working with Gray to apply a quantum modification of an existing technique to reconstruct particle tracks using IBM’s Quantum Experiment.

HEP.QPR is part of the US Department of Energy’s Quantum Information Science Enabled Discovery for High Energy Physics (QuantISED) portfolio. To learn more about the HEP.QPR project or individual student projects, visit the Berkeley Lab post here.

Header image: Lucy Linder at CERN. Image courtesy of Lucy Linder via Berkeley Lab.

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