CaltechAUTHORS
  A Caltech Library Service

Charged particle tracking with quantum annealing-inspired optimization

Zlokapa, Alexander and Anand, Abhishek and Vlimant, Jean-Roch and Duarte, Javier M. and Job, Joshua and Lidar, Daniel and Spiropulu, Maria (2019) Charged particle tracking with quantum annealing-inspired optimization. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200218-124551138

[img] PDF - Submitted Version
See Usage Policy.

3146Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200218-124551138

Abstract

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling 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 and to HL-LHC conditions. Furthermore, 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 dataset are presented, demonstrating the successful application of a quantum annealing-inspired 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 LHC while leaving open the possibility of a quantum speedup for tracking.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1908.04475arXivDiscussion Paper
ORCID:
AuthorORCID
Spiropulu, Maria0000-0001-8172-7081
Additional Information:Part of this work was conducted at “iBanks,” the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of “iBanks.” This work is partially supported by DOE/HEP QuantISED program grant, Quantum Machine Learning and Quantum Computation Frameworks (QMLQCF) for HEP, award number de-sc0019227. JMD is supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The work is also supported in part by the AT&T Foundry Innovation Centers through INQNET, a program for accelerating quantum technologies. The work of DL and JJ was partially supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the U.S. Army Research Office contract W911NF-17-C-0050.
Group:INQNET
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019227
Department of Energy (DOE)DE-AC02-07CH11359
AT&T Foundry Innovation CentersUNSPECIFIED
Intelligence Advanced Research Projects Activity (IARPA)UNSPECIFIED
Army Research Office (ARO)W911NF-17-C-0050
nVidiaUNSPECIFIED
SuperMicroUNSPECIFIED
Kavli FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20200218-124551138
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200218-124551138
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101326
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Feb 2020 21:32
Last Modified:18 Feb 2020 21:32

Repository Staff Only: item control page