A Pattern Recognition Algorithm for Quantum Annealers

Journal Article · 08 December 2019 · Computing and Software for Big Science

DOI: https://doi.org/10.1007/s41781-019-0032-5 · OSTI ID:1619431

Bapst, Frédéric ; Bhimji, Wahid; Calafiura, Paolo; Gray, Heather ; Lavrijsen, Wim; Linder, Lucy; Smith, Alex

Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1619431
• Journal Information: Computing and Software for Big Science, Journal Name: Computing and Software for Big Science Journal Issue: 1 Vol. 4; ISSN 2510-2036
• Publisher: SpringerCopyright Statement
• Country of Publication: Germany
• Language: English