60 citations · 202 across the 16 of their papers we have counts for
16 papers
Hardware-efficient quantum simulation of intense-field QED
Zhuoyi Li, Bin Xu, Zhongtian Dong +4
Strong electromagnetic backgrounds make quantum electrodynamics a real-time nonperturbative problem involving dressed fermions and dynamical photons. We propose a trapped-ion proto…
Quantum Sensing Radiative Decays of Neutrinos and Dark Matter Particles
Zhongtian Dong, Doojin Kim, Kyoungchul Kong +2
We explore a novel strategy for detecting the radiative decay of very weakly interacting particles by leveraging the extreme sensitivity of quantum devices, such as superconducting…
Hybrid quantum-classical approach for combinatorial problems at hadron colliders
Jacob L. Scott, Zhongtian Dong, Taejoon Kim +6
In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatori…
Analytical Insights on Hadronic Top Quark Polarimetry
Zhongtian Dong, Dorival Gonçalves, Kyoungchul Kong +2
Top quark polarization provides an important tool for studying its production mechanisms, spin correlations, top quark properties, and new physics searches. Unlike lighter quarks,…
Hadronic Top Quark Polarimetry with ParticleNet
Zhongtian Dong, Dorival Gonçalves, Kyoungchul Kong +2
Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize…
Quantum Vision Transformers for Quark-Gluon Classification
Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong +8
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the mul…
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resource…
is all you need: production of multiple semi-invisible resonances at hadron colliders
Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev +1
The stransverse mass variable was originally proposed for the study of hadron collider events in which parent particles are produced and then decay semi-invisibly. H…
Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against thei…
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from…
Probing the CP Structure of the Top Quark Yukawa at the Future Muon Collider
Morgan E. Cassidy, Zhongtian Dong, Kyoungchul Kong +3
We study the top-Higgs coupling with a CP violating phase at a future multi-TeV muon collider. We focus on processes that are directly sensitive to the top quark Yukawa couplin…
When the Machine Chimes the Bell: Entanglement and Bell Inequalities with Boosted
Zhongtian Dong, Dorival Gonçalves, Kyoungchul Kong +1
The Large Hadron Collider provides a unique opportunity to study quantum entanglement and violation of Bell inequalities at the highest energy available today. In this paper, we wi…
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev +1
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology. As a…
Directly Probing the CP-structure of the Higgs-Top Yukawa at HL-LHC and Future Colliders
Rahool Kumar Barman, Morgan E. Cassidy, Zhongtian Dong +8
Constraining the Higgs boson properties is a cornerstone of the LHC program and future colliders. In this Snowmass contribution, we study the potential to directly probe the Higgs-…
Resolving Combinatorial Ambiguities in Dilepton Event Topologies with Neural Networks
Haider Alhazmi, Zhongtian Dong, Li Huang +3
We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dilepto…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…