Quantum Computing and Quantum Machine Learning Algorithms for High Energy Physics

Session

Parallel Session

2 Jun 2022, 09:00

Conveners

Parallel Session

• Yen-Chi Chen (Brookhaven Laboratory)

Contribution list Timetable

9. Application of Quantum Machine Learning at the LHC

Shaojun sun (University of Wisconsin)

02/06/2022, 09:00

Oral Presentation

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

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

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

Yuan Feng (UC Berkeley)

02/06/2022, 10:00

Oral Presentation

8. Quantum Anomaly Detection for Collider Physics

Mr Avli Sulaiman (Berkeley Laboratoy)

02/06/2022, 10:20

Oral Presentation

11. Quantum Anomaly Detection for Collider Physics

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

Oral Presentation

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...