Charged particle tracking with quantum annealing optimization
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- California Institute of Technology (CalTech), Pasadena, CA (United States)
- Harvard Univ., Cambridge, MA (United States)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of California, San Diego, CA (United States)
- Lockheed Martin Advanced Technology Center, Sunnyvale, CA (United States)
- Univ. of Southern California, Los Angeles, CA (United States)
At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1599326
- Alternate ID(s):
- OSTI ID: 1828631
- Report Number(s):
- FERMILAB-PUB--19-670-PPD; arXiv:1908.04475; oai:inspirehep.net:1749369
- Journal Information:
- Quantum Machine Intelligence, Journal Name: Quantum Machine Intelligence Journal Issue: 2 Vol. 3; ISSN 2524-4906
- Publisher:
- Springer NatureCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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