A pattern recognition algorithm for quantum annealers

Bapst, Frédéric (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Bhimji, Wahid (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) ; Calafiura, Paolo (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) ; Gray, Heather (Lawrence Berkeley National Laboratory, Berkeley, CA, USA ; University of California, Berkeley, USA) ; Lavrijsen, Wim (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) ; Linder, Lucy (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Smith, Alex (University of California, Berkeley, USA)

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.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Date:
2019-12
Pagination:
7 p.
Published in:
Computing and Software for Big Science
Numeration (vol. no.):
2020, no. 4, article no. 1
DOI:
ISSN:
2510-2036
Appears in Collection:



 Record created 2020-12-15, last modified 2020-12-16

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