Quantum Computing and Quantum Machine Learning Algorithms for High Energy Physics

Contribution List

6. Quantum Compiling, Black Holes, and a Quantum LHC

Dr Andrew Sornborger (Los Alamos National Laboratory)

01/06/2022, 09:00

Quantum Computing and Machine Learning: Status and Prospects

2. Quantum Computing: Challenges and Prospects

Dr Simone Severini (University College London and Amazon Web Service)

01/06/2022, 10:00

Quantum Computing and Machine Learning: Status and Prospects

3. Overview of Classical Machine Learning

Dr Benjamin Nachman (Lawrence Berkley National Laboratory), Dr Benjamin Nachman

01/06/2022, 11:00

Quantum Computing and Machine Learning: Status and Prospects

4. Overview of Quantum Machine Learning

Dr Iordanis Kerenidis (QC Ware and CNRS)

01/06/2022, 12:00

Quantum Computing and Machine Learning: Status and Prospects

7. Quantum Neural Networks and their application to generative learning

Dr Christa Zoufal, Dr Francesco Tacchino

01/06/2022, 14:00

Oral Presentation

Quantum Algorithms: Status and Prospects

12. Introduction to Hybrid Quantum-Classical Machine Learning

Dr Yen-Chi Chen

01/06/2022, 14:50

Quantum Algorithms: Status and Prospects

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...

9. Application of Quantum Machine Learning at the LHC

Shaojun sun (University of Wisconsin)

02/06/2022, 09:00

Oral Presentation

Parallel Session

In this talk I summarize my work on the application of Application of Quantum Machine Learning on High Energy Physics Analysis at the LHC.

10. Quantum K-Means Algorithm for Signal Processing and Quantum Entanglement in Higgs Boson Decay to Four Leptons

Daniel Qenani (Yale University)

02/06/2022, 09:20

Oral Presentation

Parallel Session

13. Implementation and Analysis of Quantum Search Methods for Higgs Boson Reconstruction at the LHC

Anthony Armenakas (Harvard University)

02/06/2022, 09:40

Oral Presentation

Parallel Session

14. Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Yuan Feng (UC Berkeley)

02/06/2022, 10:00

Oral Presentation

Parallel Session

8. Quantum Anomaly Detection for Collider Physics

Mr Avli Sulaiman (Berkeley Laboratoy)

02/06/2022, 10:20

Oral Presentation

Parallel Session

11. Quantum Anomaly Detection for Collider Physics

Sulaiman Alvi (member@berkeley.edu;affiliate@berkeley.edu)

Oral Presentation

Parallel Session

Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an...