arXiv subjects

Heechan Yi

Publications and source records attributed to Heechan Yi.

3 recordsLinked to original sources

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↗

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↗

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↗