arXiv subjects

Kyoungchul Kong

Publications and source records attributed to Kyoungchul Kong.

At least 19 recordsLinked to original sources

arXiv2026

High-Energy Nuclear Recoils from Boosted Dark Matter for the LZ 248-keV Event: Beyond the Halo-Dependent High-Velocity Tail

The LUX-ZEPLIN (LZ) Collaboration has reported a nuclear-recoil candidate at $E_R=248\pm23_{\rm stat}\pm23_{\rm sys}$ keV, with a maximum local significance of $3.4\sigma$ and a global significance of $2.6\sigma$. A prominent interpretation invokes heavy halo dark matter near an inelastic threshold and therefore depends sensitively on the poorly constrained high-speed tail of the Galactic velocity distribution. In this Letter, we propose a qualitatively different possibility based on light boosted dark matter (BDM), whose incident energy is determined primarily by the dark-sector mass spectrum. We consider multi-component scenarios in which the boosted state scatters elastically or inelastically off xenon nuclei. For elastic scattering, pseudoscalar-mediated momentum dependence suppresses low-energy recoils. Near-threshold endothermic scattering of a nearly monochromatic BDM flux can instead confine the signal between kinematically determined recoil endpoints, suppressing events in both the low- and high-energy sidebands. The upscattered state may furthermore decay invisibly within the dark sector, preserving a single-nuclear-recoil signature without requiring it to be detector-stable. We present representative benchmark spectra and discuss complementary tests using other target nuclei and large-volume liquid-scintillator experiments.

hep-ph↗

arXiv2026

High-Frequency Gravitational Wave Detection with Superconducting Qubits

High-frequency gravitational waves (HFGWs) provide a unique window into high-energy and early-universe physics, yet they evade traditional macroscopic interferometry. To bridge this detection gap, we propose a novel quantum-sensing paradigm utilizing superconducting transmon qubits embedded in resonant microwave cavities. Through the inverse Gertsenshtein effect, HFGWs propagating in a static magnetic field resonantly excite a cavity mode. By leveraging the characteristic spin-2 quadrupolar pattern of the induced electromagnetic field, we position qubits directly at the electric-field hot spots of the $\mathrm{TE}_{212}$ mode to act as localized sensors. Crucially, configuring this array as an entangled quantum register via symmetric Dicke states unlocks a fundamental scaling advantage: the signal probability scales quadratically with the qubit number, translating to a $h_{\min} \propto n_q^{-3/4}$ strain sensitivity scaling. We demonstrate that an idealized global register of 800 qubits reaches a strain sensitivity that surpasses standard macroscopic cavity-power limits by five orders of magnitude. Benchmarked against representative axion-haloscope parameters, this collective quantum enhancement decisively mitigates the profound Planck-scale suppression inherent to gravitational interactions, establishing a transformative framework for next-generation HFGW searches in the GHz band.

hep-ph↗

arXiv2026

Quantum Simulation of the Real-time Dynamics in the multi-flavor Gross-Neveu Model at the utility scale using Superconducting Quantum Computers

We present a scalable quantum simulation framework for real-time dynamics of the multi-flavor Gross-Neveu model in 1+1 dimensions. Using superconducting quantum processors at utility scale, we develop a hardware-efficient Trotterization whose per-step circuit depth scales with fermion flavor number rather than total system size, enabling simulations beyond 100 qubits. A central contribution of this work is the Localized Diagonal Operator Approximation (LDOA), which systematically reduces the overhead associated with quartic interactions. We formulate diagonal unitary synthesis as a structured least-squares problem in phase space and obtain analytic solutions via the Moore-Penrose pseudoinverse. This formulation provides a principled and quantitatively controlled approximation: in the small Trotter-step regime, the unitary error is directly linked to the phase reconstruction error and vanishes asymptotically as the Trotter step size decreases. This establishes a clear mathematical foundation for the LDOA while significantly reducing two-qubit gate counts and circuit depth, and is broadly applicable to diagonal quantum operators with long-range structure, making it particularly well suited for quantum hardware with limited qubit connectivity. Using these techniques, we run large-scale simulations on IBM superconducting processors and study real-time observables, including density-density correlators. We benchmark against exact diagonalization and tensor network-based methods, finding strong agreement across system sizes. These results show that combining hardware-aware circuit design with rigorous approximations enables practical near-term simulation of interacting fermionic field theories and provides a scalable pathway toward more complex quantum field theory simulations.

quant-ph↗

arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗

arXiv2025

Direct Detection of Mechanism-Agnostic Fast-Moving Dark Matter

We present a comprehensive framework for interpreting electron recoil signals induced by fast-moving dark matter (DM), applicable across a wide range of theoretically motivated models. Amid both null results in conventional weakly interacting massive particle searches and growing interest in alternative DM scenarios, we focus on (semi-)relativistic DM components that can arise from mechanisms such as DM annihilation, decay, or cosmic-ray acceleration. These boosted DM candidates produce distinct experimental signatures that differ qualitatively from non-relativistic DM, necessitating a dedicated treatment. Our framework incorporates relativistic kinematics and atomic effects through ionization form factors, enabling accurate predictions of differential cross sections in both low- and high-energy regimes. We demonstrate how atomic effects become negligible at high recoil energies, validating the free-electron approximation in specific parameter regions. Furthermore, we highlight the complementarity between low-threshold direct detection experiments and high-threshold neutrino observatories in probing fast-moving DM across broad kinematic domains. This formalism provides a robust and model-independent foundation for interpreting current and future searches for relativistic DM.

hep-ph↗

arXiv2025

Quantum Integration Networks for Efficient Monte Carlo in High-Energy Physics

Monte Carlo methods play a central role in particle physics, where they are indispensable for simulating scattering processes, modeling detector responses, and performing multi-dimensional integrals. However, traditional Monte Carlo methods often suffer from slow convergence and insufficient precision, particularly for functions with singular features such as rapidly varying regions or narrow peaks. Quantum circuits provide a promising alternative: compared to conventional neural networks, they can achieve rich expressivity with fewer parameters, and the parameter-shift rule provides an exact analytic form for circuit gradients, ensuring precise optimization. Motivated by these advantages, we investigate how sampling strategies and loss functions affect integration efficiency within the \textbf{Quantum Integration Network} (QuInt-Net). We compare adaptive and non-adaptive sampling approaches and examine the impact of different loss functions on accuracy and convergence. Furthermore, we explore three quantum circuit architectures for numerical integration: the data re-uploading model, the quantum signal processing protocol, and deterministic quantum computation with one qubit. The results provide new insights into optimizing QuInt-Nets for applications in high energy physics.

quant-ph↗

arXiv2025

Quantum Sensing Radiative Decays of Neutrinos and Dark Matter Particles

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 transmon qubits and trapped ion systems, to faint electromagnetic signals. By modeling the effective electric field induced by the decay photons, we evaluate the response of quantum sensors across two particle physics scenarios: the cosmic neutrino background and two-component dark matter. We assess the discovery potential of these devices and outline the parameter space accessible under current experimental capabilities. Our analysis demonstrates that quantum sensors can probe radiative decays of dark matter candidates using existing technology, while probing neutrino magnetic moments beyond current limits will require scalable quantum architectures with enhanced coherence.

hep-ph↗

arXiv2025

Design Initiative for a 10 TeV pCM Wakefield Collider

This document outlines a community-driven Design Study for a 10 TeV pCM Wakefield Accelerator Collider. The 2020 ESPP Report emphasized the need for Advanced Accelerator R\&D, and the 2023 P5 Report calls for the ``delivery of an end-to-end design concept, including cost scales, with self-consistent parameters throughout." This Design Study leverages recent experimental and theoretical progress resulting from a global R\&D program in order to deliver a unified, 10 TeV Wakefield Collider concept. Wakefield Accelerators provide ultra-high accelerating gradients which enables an upgrade path that will extend the reach of Linear Colliders beyond the electroweak scale. Here, we describe the organization of the Design Study including timeline and deliverables, and we detail the requirements and challenges on the path to a 10 TeV Wakefield Collider.

physics.acc-ph↗

arXiv2025

Reinterpretation and preservation of data and analyses in HEP

Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental collaborations that obtain them, it is imperative that the collaborations and the wider particle physics community publish and preserve sufficient information to ensure that this impact can be realised, now and into the future. The information to be published and preserved includes the algorithms, statistical information, simulations and the recorded data. This publication and preservation requires significant resources, and should be a strategic priority with commensurate planning and resource allocation from the earliest stages of future facilities and experiments.

hep-ph↗

arXiv2025

Direct Detection of Fast-Moving Low-Mass Dark Matter

We examine the signals produced by dark matter interactions with electrons, which play a crucial role in direct detection experiments employing heavy target materials, particularly in many well-motivated sub-GeV dark matter scenarios. When the momentum transfer to target electrons is comparable to or exceeds their binding energy, atomic effects related to electron ionization become essential for accurately determining signal rates - especially in the case of fast-moving dark matter. In this paper, we revisit and extend the atomic ionization formalism, systematically comparing different approaches used to formulate the ionization form factor and identifying their respective domains of validity. As practical applications, we explore detection prospects in xenon target experiments. To illustrate our findings, we consider a specific scenario involving boosted dark matter, which often leads to high-momentum electron recoils. Our analysis demonstrates that the choice of formalism can significantly influence the interpretation of experimental data, depending on the regions of parameter space.

hep-ph↗

arXiv2025

Quantum generative adversarial networks for gluon initiated jets generation

Quantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics (HEP) simulations. This work presents the implementation of a Quantum Generative Adversarial Network (qGAN) to simultaneously generate gluon-initiated jet images for both ECAL and HCAL detector channels, a task crucial for high-energy physics simulations at the Large Hadron Collider (LHC). The results demonstrate high fidelity in replicating energy deposit patterns and preserving the implicit training data features. This study marks the first step toward generating multi-channel pictures and quark-initiated jet images using quantum computing.

physics.comp-ph↗

arXiv2024

Quantum Diffusion Model for Quark and Gluon Jet Generation

Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems.

quant-ph↗

arXiv2024

Lie-Equivariant Quantum Graph Neural Networks

Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that is not only data efficient, but also has symmetry-preserving properties. Since Lorentz group equivariance has been shown to be beneficial for jet tagging, we build a Lorentz-equivariant quantum GNN for quark-gluon jet discrimination and show that its performance is on par with its classical state-of-the-art counterpart LorentzNet, making it a viable alternative to the conventional computing paradigm.

quant-ph↗

arXiv2024

Quantum Attention for Vision Transformers in High Energy Physics

We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications. Building on advancements in quantum vision transformers, our approach addresses limitations of prior models by leveraging the inherent advantages of QONNs, including stability and efficient parameterization in high-dimensional spaces. We evaluate the proposed architecture using multi-detector jet images from CMS Open Data, focusing on the task of distinguishing quark-initiated from gluon-initiated jets. The results indicate that embedding quantum orthogonal transformations within the attention mechanism can provide robust performance while offering promising scalability for machine learning challenges associated with the upcoming High Luminosity Large Hadron Collider. This work highlights the potential of quantum-enhanced models to address the computational demands of next-generation particle physics experiments.

quant-ph↗

arXiv2024

Hybrid quantum-classical approach for combinatorial problems at hadron colliders

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 combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA), a feedback-based algorithm (FALQON) and a variational quantum imaginary time evolution algorithm (VarQITE). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit adaptability and the potential for scalability, making them promising candidates for a variety of high-energy physics applications, as quantum hardware technology matures. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data.

hep-ph↗

arXiv2024

Astrophysical and Cosmological Probes of Boosted Dark Matter

We present an in-depth study of two-component cold dark matter via extensive N-body simulations. We examine various cosmological observables including the temperature evolution, power spectrum, density perturbation, maximum circular velocity functions, and galactic density profiles. We find that a significant mass difference between the two components, coupled with the annihilation of the heavier into the lighter component, imparts warm dark matter (WDM)-like characteristics to the latter. This model benefits from the unique features of WDM, such as modifications to the matter power spectrum and density profiles, while avoiding stringent observational constraints on WDM mass. The two-component dark-matter model aligns with observational data and suggests new avenues for dark-matter detection in terrestrial experiments, particularly for light, sub-MeV DM candidates. Our findings provide a framework for understanding the small-scale structures and offer guidance for future particle physics and cosmological studies.

hep-ph↗

arXiv2024

Probing the CP Structure of the Top Quark Yukawa at the Future Muon Collider

We study the top-Higgs coupling with a CP violating phase $ξ$ at a future multi-TeV muon collider. We focus on processes that are directly sensitive to the top quark Yukawa coupling: $t\bar{t}h$, $tbhμν$, and $t\bar{t}hν\barν$ with $h\rightarrow b\bar{b}$ and semileptonic top decays. At different energies, different processes dominate the cross section, providing complementary information. At and above an energy of $\mathcal{O}(10)$ TeV, vector boson fusion processes dominate. As we show, in the Standard Model there is destructive interference in the vector boson fusion processes $t\bar{t}hν\barν$ and $tbhμν$ between the top quark Yukawa and Higgs-gauge boson couplings. A CP-violating phase changes this interference, and the cross section measurement is very sensitive to the size of the CP-violating angle. Although we find that the cross sections are measured to $\mathcal{O}(50\%)$ statistical uncertainty at $1σ$, a 10 and 30 TeV muon collider can bound the CP-violating angle $|ξ|\lesssim9.0^\circ$ and $|ξ|\lesssim5.4^\circ$, respectively. However, cross section measurements are insensitive to the sign of the CP-violating angle. To determine that the coupling is truly CP violating, observables sensitive to CP-violation must be measured. We find in the $t\bar{t}h$ process the azimuthal angle between the $t+\bar{t}$ plane and the initial state muon+Higgs plane shows good discrimination for $ξ=\pm0.1π$. For the $tbhμν$ and $t\bar{t}hν\barν$ processes, the operator proportional to $\left(\vec{p}_μ\times\vec{p}_h\right)\cdot \vec{p}_t$ is sensitive to the sign of CP phase $ξ$. From these observables, we construct asymmetry parameters that show good distinction between different values and signs of the CP violating angle.

hep-ph↗

arXiv2024

Analytical Insights on Hadronic Top Quark Polarimetry

Top quark polarization provides an important tool for studying its production mechanisms, spin correlations, top quark properties, and new physics searches. Unlike lighter quarks, the top quark's polarization remains intact until its decay, enabling precise spin measurements. While the down-type fermions from $W$ boson decay are known to be effective spin analyzers, charged leptons have typically been the main target for most analyses. In this paper, we investigate the relevance of global jet dynamics -- considering kinematics, jet charges, and particle multiplicity -- for hadronic top quark polarimetry. The formalism used allows for analytical derivations obtained throughout the manuscript, offering deeper insights into the corresponding phenomenology.

hep-ph↗