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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.
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Edward Farhi, Jeffrey Goldstone and Sam Gutmann · 2002
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A. Pulvirenti et al · 2004
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“A recurrent neural network for track reconstruction in the LHCb Muon System”
G. Passaleva · 2008
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“Minor-embedding in adiabatic quantum computation: I. The parameter setting problem”
Vicky Choi · 2008
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“Track and vertex reconstruction: From classical to adaptive methods”
Are Strandlie and Rudolf Fruhwirth · 2010
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“Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design”
Vicky Choi · 2011
Cited alongside, same era.
“Identification of b-Quark Jets with the CMS Experiment”
Serguei Chatrchyan · 2013
Cited alongside, same era.
“Parallel track reconstruction in CMS using the cellular automaton approach”
Daniel Funke et al · 2014
Cited alongside, same era.
“Description and performance of track and primary-vertex reconstruction with the CMS tracker”
The Collaboration · 2014
Cited alongside, same era.
URL: http://cds.cern.ch/record/1751454
“Pileup Removal Algorithms”, 2014 · 2014
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“Ising formulations of many NP problems”
A. Lucas · 2014
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“Solving a Higgs optimization problem with quantum annealing for machine learning”
Alex Mott et al · 2017
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“Particle-flow reconstruction and global event description with the CMS detector”
A.. Sirunyan · 2017
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“The CMS trigger system”
V. Khachatryan et al · 2017
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In TrackingPOGPerformance2017MC < CMSPublic < TWiki , 2017
“CMS Tracking POG Performance Plots For 2017 with PhaseI pixel detector” · 2017
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“An accelerator architecture for combinatorial optimization problems”
Sanroku Tsukamoto, Motomu Takatsu, Satoshi Matsubara and Hirotaka Tamura · 2017
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“Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures”
G Cerati et al · 2018
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“Evidence for quantum annealing with more than one hundred qubits”
S. Boixo et al · 2014
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“Defining and detecting quantum speedup”
Troels. Rnnow et al · 2014
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“Architectural Considerations in the Design of a Superconducting Quantum Annealing Processor”
P.. Bunyk et al · 2014
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“Adiabatic quantum programming: minor embedding with hard faults”
Christine Klymko, Blair. Sullivan and Travis. Humble · 2014
Cited alongside, same era.
“A practical heuristic for finding graph minors”
Jun Cai, William. Macready and Aidan Roy · 2014
Cited alongside, same era.
“Performance of the CMS missing transverse momentum reconstruction in pp data at s \sqrt{s} = 8 TeV”
Vardan Khachatryan · 2015
Cited alongside, same era.
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“Novel deep learning methods for track reconstruction”
Steven Farrell · 2018
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“Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing”
Tameem Albash and Daniel. Lidar · 2018
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“Quantum annealing versus classical machine learning applied to a simplified computational biology problem”
Richard. Li, Rosa Di, Remo Rohs and Daniel. Lidar · 2018
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“Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”
A.. Sirunyan · 2018
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“Measurements of b-jet tagging efficiency with the ATLAS detector using t t ¯ t\overline{t} events at s = 13 \sqrt{s}=13 TeV”
Morad Aaboud · 2018
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“TrackML: A High Energy Physics Particle Tracking Challenge”
Polo Calafiura · 2018
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“A Roadmap for HEP Software and Computing R&D for the 2020s”
The HEP Software Foundation et al · 2019
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“Performance of missing transverse momentum reconstruction in proton-proton collisions at s = \sqrt{s}= 13 TeV using the CMS detector”
Albert Sirunyan · 2019
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URL: https://www.dwavesys.com/sites/default/files/14-1037A-A_Improved_coheverbrence_leads_to_gains_QA_performance.pdf
“D-Wave White Paper: Improved coherence leads to gains in quantum annealing performance”, 2019 · 2019
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Kelly Boothby, Paul Bunyk, Jack Raymond and Aidan Roy · 2019
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