activity
20202026
most citedCooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

249 citations · 321 across the 38 of their papers we have counts for

collaborators

38 papers

quant-ph2026

Computationally Efficient Optimization of Per-Qubit Clifford Deformation for Non-uniform Biased Noise

Won Joon Yun, Andrew Nemec, Jonathan M. Baker

In fault-tolerant quantum computing systems with biased noise, Clifford deformation can substantially reduce the logical error rate (LER) without additional physical hardware overh…

quant-ph2026

IRIS: A Compiler for Distributed Quantum Systems

Won Joon Yun, Dhilan Nag, Sneha Ballabh +3

Distributed Quantum Computers (DQCs) enable large system sizes by connecting smaller chips via photonic interconnects. DQCs use teleportation to relocate qubits and execute CNOTs b…

cs.LG2023

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

Won Joon Yun, Samuel Kim, Joongheon Kim

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scruti…

cs.MA2023

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

Chanyoung Park, Haemin Lee, Won Joon Yun +2

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles…

quant-ph2023★ 1 cited

EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback

Soohyun Park, Won Joon Yun, Chanyoung Park +4

This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neura…

cs.MA2023★ 1 cited

Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems

Chanyoung Park, Won Joon Yun, Jae Pyoung Kim +4

This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmann…

quant-ph2023

Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management

Won Joon Yun, Jae Pyoung Kim, Soyi Jung +2

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational…

eess.SY2022★ 3 cited

Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems

Hyunsoo Lee, Soohyun Park, Won Joon Yun +2

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based…

quant-ph2022★ 4 cited

Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding

Won Joon Yun, Jae Pyoung Kim, Hankyul Baek +4

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum c…

quant-ph2022★ 9 cited

Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification

Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, quantum machine learning (QML) has been actively used for various tasks, e.g., classification, reinforcement learning, and adversarial learning. However, these QML…

quant-ph2022

Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning

Chanyoung Park, Jae Pyoung Kim, Won Joon Yun +3

Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quan…

quant-ph2022★ 2 cited

Quantum Split Neural Network Learning using Cross-Channel Pooling

Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum…

quant-ph2022

3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications

Hankyul Baek, Won Joon Yun, Joongheon Kim

With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classica…

cs.LG2022★ 10 cited

Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control

Chanyoung Park, Soohyun Park, Soyi Jung +2

This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work…

cs.CV2022

Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications

Won Joon Yun, Soohyun Park, Joongheon Kim +1

Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems a…

quant-ph2022★ 1 cited

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Hankyul Baek, Won Joon Yun, Joongheon Kim

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutio…

cs.CR2022

Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks

Haemin Lee, Seok Bin Son, Won Joon Yun +3

One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method. Traditional COA attack search methods that passively search for att…

quant-ph2022★ 4 cited

Scalable Quantum Convolutional Neural Networks

Hankyul Baek, Won Joon Yun, Joongheon Kim

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networ…

quant-ph2022

Quantum Multi-Agent Meta Reinforcement Learning

Won Joon Yun, Jihong Park, Joongheon Kim

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of pract…

cs.LG2022★ 10 cited

Slimmable Quantum Federated Learning

Won Joon Yun, Jae Pyoung Kim, Soyi Jung +3

Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the ex…

quant-ph2022★ 2 cited

Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design

Won Joon Yun, Yunseok Kwak, Jae Pyoung Kim +4

In recent years, quantum computing (QC) has been getting a lot of attention from industry and academia. Especially, among various QC research topics, variational quantum circuit (V…

cs.LG2022

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

Won Joon Yun, Yunseok Kwak, Hankyul Baek +5

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is chal…

quant-ph2022★ 2 cited

Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim +4

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to…

cs.CV2022★ 4 cited

Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification

Youngkee Kim, Won Joon Yun, Youn Kyu Lee +2

In many deep neural network (DNN) applications, the difficulty of gathering high-quality data in the industry field hinders the practical use of DNN. Thus, the concept of transfer…