Name: Quantum Computing and Quantum Machine Learning Algorithms for High Energy Physics
Start: 2022-06-01T09:00:00-04:00
End: 2022-06-02T16:00:00-04:00
Location: No location set

Wednesday, 1 June ¶
- Wed, 1 Jun
- Thu, 2 Jun
- Quantum Computing and Machine Learning: Status and Prospects¶
  
  Convener: Dr Shinjae Yoo (Brookhaven Laboratory (BNL))
  - 1
    
    Quantum Compiling, Black Holes, and a Quantum LHC¶
    
    Speaker: Dr Andrew Sornborger (Los Alamos National Laboratory)
    
    QML-HEP-June1-2022.pptx
  - 2
    
    Quantum Computing: Challenges and Prospects¶
    
    Speaker: Dr Simone Severini (University College London and Amazon Web Service)
    
    Simone.pptx
  - 3
    
    Overview of Classical Machine Learning¶
    
    Speakers: Dr Benjamin Nachman (Lawrence Berkley National Laboratory), Dr Benjamin Nachman
    
    QML_2022.pdf
  - 4
    
    Overview of Quantum Machine Learning¶
    
    Speaker: Dr Iordanis Kerenidis (QC Ware and CNRS)
    
    QMLWorkshop_Kerenidis.pptx
- 13:00
  
  Lunch
- Quantum Algorithms: Status and Prospects¶
  
  Convener: Prof. Oliver Baker (Yale University)
  - 5
    
    Quantum Neural Networks and their application to generative learning¶
    
    Speakers: Dr Christa Zoufal, Dr Francesco Tacchino
  - 6
    
    Introduction to Hybrid Quantum-Classical Machine Learning¶
    
    Recent advances in machine learning (ML) and quantum computing (QC) hardware draw significant attention to building quantum machine learning (QML) applications. In this talk, I will first provide an overview of the hybrid quantum-classical machine learning paradigm. Important ideas such as calculating quantum gradients will be described. Then I will present the recent progress of QML in various application fields such as classification, distributed or federated learning and reinforcement learning. Potential advantage and scalability in the NISQ era will be discussed as well. Finally, I will briefly discuss several promising research directions.
    
    Speaker: Dr Yen-Chi Chen
    
    2022_06_01_Introduction_to_QML_with_Applications.pdf
Thursday, 2 June ¶
- Wed, 1 Jun
- Thu, 2 Jun
- Parallel Session¶
  
  Convener: Dr Yen-Chi Chen (Brookhaven Laboratory)
  - 7
    
    Application of Quantum Machine Learning at the LHC¶
    
    In this talk I summarize my work on the application of Application of Quantum Machine Learning on High Energy Physics Analysis at the LHC.
    
    Speaker: Shaojun sun (University of Wisconsin)
    
    Sau-Lan-Final.pdf
  - 8
    
    Quantum K-Means Algorithm for Signal Processing and Quantum Entanglement in Higgs Boson Decay to Four Leptons¶
    
    Speaker: Daniel Qenani (Yale University)
    
    Daniel-Presentation.pdf
  - 9
    
    Implementation and Analysis of Quantum Search Methods for Higgs Boson Reconstruction at the LHC¶
    
    Speaker: Anthony Armenakas (Harvard University)
    
    Antony.pptx
  - 10
    
    Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo¶
    
    Speaker: Yuan Feng (UC Berkeley)
    
    YuanF.pdf
  - 11
    
    Quantum Anomaly Detection for Collider Physics¶
    
    Speaker: Mr Avli Sulaiman (Berkeley Laboratoy)
    
    ben.pptx
    
    QML for HEP Workshop Presentation (1).pptx
- Brainstorming Session¶
  
  Convener: Prof. Kamal Benslama (Loyola University Maryland)