(HPCWire) Researchers at Lawrence Berkeley National Laboratory are working to tackle high volumes of particle physics data with quantum computing.
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.
HEP.QPR is part of the U.S. Department of Energy’s Quantum Information Science Enabled Discovery for High Energy Physics (QuantISED) portfolio.
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.