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IEEE Quantum Week 2021 Workshop: Quantum Computing for High-Energy Physics

21 October 2021
US/Mountain timezone

Session

Workshop sessions

21 Oct 2021, 10:45

Conveners

Workshop sessions: Session 1

  • Sofia Vallecorsa (CERN)

Workshop sessions: Session 2

  • Jean-Roch Vlimant (California Institute of Technology (US))

Workshop sessions: Session 3

  • Andrea Delgado (Oak Ridge National Laboratory)

Presentation materials

There are no materials yet.

  1. 16. Introduction
    Andrea Delgado (Oak Ridge National Laboratory)
    21/10/2021, 10:45
  2. 1. Keynote: Scalable Quantum Simulations of the Schwinger Model
    Nathan Wiebe (University of Toronto)
    21/10/2021, 10:50
  3. 2. Keynote: Quantum Computing Applications for HEP
    Gabriel Perdue (Fermilab), Gabriel Perdue (Fermilab)
    21/10/2021, 11:35
  4. 12. Trends in quantum machine learning based on parametrized quantum circuits
    Vedran Dunjko (Leiden University)
    21/10/2021, 12:45
  5. 3. Dimensional Expressivity Analysis of Parametric Quantum Circuits
    Lena Funcke (Perimeter Institute)
    21/10/2021, 13:15
  6. 13. Quantum speedup for track reconstruction in particle accelerators
    Yasser Omar (University of Lisbon)
    21/10/2021, 13:35
  7. 4. First implementation of Quantum Machine Learning algorithms for b-jet tagging at LHCb
    Davide Zuliani (Universita e INFN, Padova (IT))
    21/10/2021, 14:05
  8. 5. Modified Layerwise Learning for Data Re-Uploading Classifier in High-Energy Physics Event Classification
    Eraraya Ricardo Muten
    21/10/2021, 14:45
  9. 7. Quantum Imaginary Time Evolution for Quantum Field Theories with Continuous Variables
    Yeter-Aydeniz Kubra (ORNL)
    21/10/2021, 15:05
  10. 8. Towards Quantum Compression in High-Energy Physics Data
    Carlos A. Argüelles-Delgado (Harvard University), Jeffrey Lazar (University of Wisconsin-Madison)
    21/10/2021, 15:25
  11. 9. Quantum Computing for Color Reconnection
    Prasanth Shyamsundar (Fermi National Accelerator Laboratory)
    21/10/2021, 15:45
  12. 10. Digital quantum simulation for screening and confinement in gauge theory with a topological term
    Lento Nagano (University of Tokyo (JP))
    21/10/2021, 16:05
  13. 14. First implementation of Quantum Machine Learning algorithms for b-jet tagging at LHCb
    Davide Zuliani (Universita e INFN, Padova (IT))

    The identification of jets coming from heavy-flavor quarks, namely bottom and charm quarks, is an important and non-trivial task at the LHC experiments. The classification of jets coming from bottom and anti-bottom quarks at the LHCb experiment would allow to perform physics measurements, such as the forward-central charge asymmetry, to constrain the Standard Model predictions and/or find...

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  14. 6. Quantum algorithm for the classification of supersymmetric top quark events
    Pedrame Bargassa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
  15. 11. Quantum algorithm for the classification of supersymmetric top quark events
    Pedrame Bargassa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)

    The search for supersymmetric particles is one of the major goals of the
    Large Hadron Collider (LHC). Supersymmetric top searches play a very
    important role in this respect, but the unprecedented collision rate to be
    attained at the next high luminosity phase of the LHC poses new challenges
    for the separation between any new signal and the standard model
    background. Quantum computing...

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