Israel Ferraz de Araujo

Hyeongju Kim

Kwangjun Choi

Kiwmann Hwang

Seungjik Kim

Wooseop Hwang

Junghan Lee

Sungoh Jung

Quantum Park is a research group at Yonsei University dedicated to advancing the field of quantum computing and quantum information science. Our mission is to explore fundamental principles of quantum mechanics and develop innovative quantum technologies. We focus on various aspects including quantum algorithms, quantum error correction, quantum cryptography, and the development of superconducting quantum processors. Through interdisciplinary collaboration and cutting-edge research, we aim to contribute to the global quantum revolution and educate the next generation of quantum scientists and engineers. We are an interdisciplinary research group at the intersection of statistics, physics, computer science, and applied mathematics. Our mission is captured by the theme: "Quantum Computing for Machine Learning, Machine Learning for Quantum Computing". We harness quantum information theory to tackle fundamental and practical challenges in data science, computational science, and AI. We develop machine learning techniques that combat noise and imperfections in quantum information processing tasks. We advance industrial applications of quantum computing through cutting-edge quantum optimization methods.

퀀텀 파크는 양자 컴퓨팅 및 양자 정보 과학 분야의 발전을 목표로 하는 연세대학교 연구 그룹입니다. 우리는 양자 역학의 근본 원리를 탐구하고 혁신적인 양자 기술을 개발하는 것을 목표로 합니다. 양자 알고리즘, 양자 오류 수정, 양자 암호학 및 초전도 양자 프로세서 개발을 포함한 다양한 측면에 중점을 둡니다. 학제 간 협력과 최첨단 연구를 통해 우리는 글로벌 양자 혁명에 기여하고 차세대 양자 과학자 및 엔지니어를 양성하는 것을 목표로 합니다.

Quantum convolutional neural network for classical data classification

A divide-and-conquer algorithm for quantum state preparation

Circuit-Based Quantum Random Access Memory for Classical Data

Quantum Error Mitigation With Artificial Neural Network

Classical-to-quantum convolutional neural network transfer learning

The theory of the quantum kernel-based binary classifier

Linear-depth quantum circuits for multiqubit controlled gates

Circuit-Based Quantum Random Access Memory for Classical Data With Continuous Amplitudes

Estimating the Coherence of Noise in Quantum Control of a Solid-State Qubit

Recent advances in nuclear magnetic resonance quantum information processing

Configurable sublinear circuits for quantum state preparation

Quantum-inspired algorithm for direct multi-class classification

Tyrosine phosphatases Shp1 and Shp2 have unique and opposing roles in oligodendrocyte development

Quantum readout error mitigation via deep learning

The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices

Heat Bath Algorithmic Cooling with Spins: Review and Prospects

Variational quantum one-class classifier

Hyperfine spin qubits in irradiated malonic acid: heat-bath algorithmic cooling

Compact quantum kernel-based binary classifier

Hierarchical quantum circuit representations for neural architecture search

Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling

Diabatic quantum annealing for training energy-based generative models

Quantum-classical hybrid approach for codon optimization and its practical applications

Multi-channel convolutional neural quantum embedding

Neural Quantum Embedding via Deterministic Quantum Computation with One Qubit

Schmidt quantum compressor

Understanding generalization in quantum machine learning with margins

Hamiltonian formulations of centroid-based clustering

Optimizing quantum convolutional neural network architectures for arbitrary data dimension

Expressivity of deterministic quantum computation with one qubit

Early-stage detection of cognitive imparement by hybrid quantum-classical algorithm using resting-state functional MRI time-series

Distributed quantum machine learning via classical communication

Quantum support vector data description for anomaly detection

Neural quantum embedding: Pushing the limits of quantum supervised learning

Quadratic speed-ups in quantum kernelized binary classifier

Quantum variational distance-based centroid classifier

Scalable quantum measurement error mitigation via conditional independence and transfer learning

Quantum Park

교수님 소개

박경덕

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling

Diabatic quantum annealing for training energy-based generative models

Quantum-classical hybrid approach for codon optimization and its practical applications

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

박경덕

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling

Diabatic quantum annealing for training energy-based generative models

Quantum-classical hybrid approach for codon optimization and its practical applications

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집