Berkeley Lab to Tackle Particle Physics with Quantum Computing

By Staff report

February 18, 2020

Massive-scale particle physics produces correspondingly large amounts of data – 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 receive a massive upgrade through the High Luminosity LHC (HL-LHC) Project. This will increase the LHC’s data output by five to seven times – billions of particle events every second – and researchers are scrambling to prepare big data computing for this deluge of particle physics data. Now, researchers at Lawrence Berkeley National Laboratory are working to tackle high volumes of particle physics data with quantum computing.

When a particle accelerator runs, particle detectors offer data points for where particles crossed certain thresholds in the accelerator. Researchers then attempt to reconstruct precisely how the particles traveled through the accelerator, typically using some form of computer-aided pattern recognition. 

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

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

Crucially, HEP.QPR’s work also incorporates student researchers. In a recent blog post, Berkeley Lab highlighted the work of several of these researchers. Lucy Linder, a master’s student, wrote her thesis on the application of quantum annealing for finding particle tracks while working with HEP.QPR; Eric Rohm, an undergraduate, developed a quantum approximate optimization algorithm (QAOA) while participating in the DOE’s Science Undergraduate Laboratory Internship Program; and Amitabh Yadav, a student research associate at Berkeley Lab, is working with Gray to apply a quantum modification of an existing technique to reconstruct particle tracks using IBM’s Quantum Experience.

HEP.QPR is part of the U.S. Department of Energy’s Quantum Information Science Enabled Discovery for High Energy Physics (QuantISED) portfolio. To read more about the HEP.QPR project or the 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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