Publications de Su Yeon Chang | EPFL Graph Search

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

Su Yeon Chang

Geometric quantum machine learning based on equivariant quantum neural networks (EQNNs) recently appeared as a promising direction in quantum machine learning. Despite encouraging progress, studies are still limited to theory, and the role of hardware nois ...

AMER PHYSICAL SOC2024

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Su Yeon Chang

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

EPFL2024

Impact of quantum noise on the training of quantum Generative Adversarial Networks

Su Yeon Chang

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this ...

IOP PUBLISHING LTD2023

Running the Dual-PQC GAN on noisy simulators and real quantum hardware

Su Yeon Chang

In an earlier work [1], we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simula ...

IOP PUBLISHING LTD2023

Quantum Convolutional Circuits For Earth Observation Image Classification

Su Yeon Chang

The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Ear ...

IEEE2022

Bell Diagonal and Werner State Generation: Entanglement, Non-Locality, Steering and Discord on the IBM Quantum Computer

Nicolas Macris, Su Yeon Chang, Marc-André Dupertuis, Chun Lam Chan, Samuel Bosch, Nicolas Schwaller, Frédéric Gessler

We propose the first correct special-purpose quantum circuits for preparation of Bell diagonal states (BDS), and implement them on the IBM Quantum computer, characterizing and testing complex aspects of their quantum correlations in the full parameter spac ...

2021

Dynamical Localization Simulated on Actual Quantum Hardware

Su Yeon Chang

Quantum computers are invaluable tools to explore the properties of complex quantum systems. We show that dynamical localization of the quantum sawtooth map, a highly sensitive quantum coherent phenomenon, can be simulated on actual, small-scale quantum pr ...

MDPI2021

Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Su Yeon Chang

Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GA ...

E D P Sciences2021