29 July 2019 to 2 August 2019
Northeastern University
US/Eastern timezone

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

1 Aug 2019, 14:40
20m
Shillman 425 (Northeastern University)

Shillman 425

Northeastern University

Oral Presentation Computing, Analysis Tools, & Data Handling Computing, Analysis Tools, & Data Handling

Speaker

Alex Zeng Wang (University of Wisconsin Madison (US))

Description

Using IBM Quantum Computer Simulators and Quantum Computer Hardware, we have successfully employed the Quantum Support Vector Machine Method (QSVM) for a ttH (H to two photons), Higgs coupling to top quarks analysis at LHC.

We will present our experiences and results of a study on LHC high energy physics data analysis with IBM Quantum Computer Simulators and IBM Quantum Computer Hardware using IBM Qiskit. The work is in the context of a Qubit platform. Taking into account the limitation of a low number of qubits, the result expressed in ROC curve is comparable with the results using a classical machine learning method. This study is applied to a physics channel where Higgs-coupling-to–two-top-quarks (ttH), one of the flagship physics channels at LHC. Here ROC curve is defined as the Receiver Operating Characteristics curve on the plane of background rejection versus signal efficiency. At our current stage, with 5 qubits and 800 events we have reached AUC of 0.86, which is similar to the AUC of 0.87 from classical machine learning method (BDT), where AUC is the area under the ROC curve. By the time of the conference, we expect to have results performed by 20 qubits.

In addition, collaborating with IBM Research Zurich, we finished training with machine learning on the IBM Quantum Computer Hardware with 100 training events, 100 test events, and 5 qubits, again for ttH (H to two photons) analysis at LHC. Because of hardware access time and timeout limitations, we only finished a few iterations. By the time of the conference, we expect to perform the study on 20 qubits hardware with large number of iterations.

The work is performed with an international and interdisciplinary collaboration with high energy physicists (Physics Department, University of Wisconsin), computational scientists (Computing Science Department, University of Wisconsin and CERN openlab, IT Department), and quantum computing scientists (IBM Research Zurich).

This work pioneers a close collaboration of academic institutions with industrial corporations in High Energy Physics analysis effort.

Primary authors

Jay Chan (University of Wisconsin Madison (US)) Wen Guan (University of Wisconsin (US)) Shaojun Sun (University of Wisconsin Madison (US)) Alex Zeng Wang (University of Wisconsin Madison (US)) Sau Lan Wu (University of Wisconsin Madison (US)) Chen Zhou (University of Wisconsin Madison (US)) Miron Livny (University of Wisconsin-Madison) Federico Carminati (CERN) Alberto Di Meglio (CERN) Panagiotis Barkoutsos (IBM Research - Zurich) Ivano Tavernelli (IBM Research - Zurich) Stefan Woerner (IBM Research - Zurich) Christa Zoufal (IBM Research - Zurich)

Presentation Materials