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
- 09:00 → 13:00
  Quantum Computing and Machine Learning: Status and Prospects
  
  Convener: Dr Shinjae Yoo (Brookhaven Laboratory (BNL))
  - 09:00
    
    Quantum Compiling, Black Holes, and a Quantum LHC 50m
    
    Speaker: Dr Andrew Sornborger (Los Alamos National Laboratory)
    
    QML-HEP-June1-2022.pptx
  - 10:00
    
    Quantum Computing: Challenges and Prospects 50m
    
    Speaker: Dr Simone Severini (University College London and Amazon Web Service)
    
    Simone.pptx
  - 11:00
    
    Overview of Classical Machine Learning 50m
    
    Speakers: Dr Benjamin Nachman (Lawrence Berkley National Laboratory), Dr Benjamin Nachman
    
    QML_2022.pdf
  - 12:00
    
    Overview of Quantum Machine Learning 50m
    
    Speaker: Dr Iordanis Kerenidis (QC Ware and CNRS)
    
    QMLWorkshop_Kerenidis.pptx
- 13:00 → 14:00
  
  Lunch 1h
- 14:00 → 17:00
  Quantum Algorithms: Status and Prospects
  
  Convener: Prof. Oliver Baker (Yale University)
  - 14:00
    
    Quantum Neural Networks and their application to generative learning 30m
    
    Speakers: Dr Christa Zoufal, Dr Francesco Tacchino
  - 14:50
    
    Introduction to Hybrid Quantum-Classical Machine Learning 30m
    
    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
- 09:00 → 11:05
  Parallel Session
  
  Convener: Dr Yen-Chi Chen (Brookhaven Laboratory)
  - 09:00
    
    Application of Quantum Machine Learning at the LHC 20m
    
    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
  - 09:20
    
    Quantum K-Means Algorithm for Signal Processing and Quantum Entanglement in Higgs Boson Decay to Four Leptons 20m
    
    Speaker: Daniel Qenani (Yale University)
    
    Daniel-Presentation.pdf
  - 09:40
    
    Implementation and Analysis of Quantum Search Methods for Higgs Boson Reconstruction at the LHC 20m
    
    Speaker: Anthony Armenakas (Harvard University)
    
    Antony.pptx
  - 10:00
    
    Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo 20m
    
    Speaker: Yuan Feng (UC Berkeley)
    
    YuanF.pdf
  - 10:20
    
    Quantum Anomaly Detection for Collider Physics 20m
    
    Speaker: Mr Avli Sulaiman (Berkeley Laboratoy)
    
    ben.pptx
    
    QML for HEP Workshop Presentation (1).pptx
- 11:05 → 12:05
  
  Brainstorming Session
  
  Convener: Prof. Kamal Benslama (Loyola University Maryland)