25 citations · 116 across the 11 of their papers we have counts for
13 papers
Practical fermionic shadows enabled by improved sample-complexity bounds
Maxwell West, Su Yeon Chang, Luke Coffman +2
Classical shadow tomography is widely touted as supplying a family of methods for extracting information from quantum systems with polynomially scaling sample-complexities. In the…
The cost of simulating classically tractable quantum circuits and dynamics
Su Yeon Chang, Supanut Thanasilp, Zoë Holmes +1
Determining whether a quantum evolution can be efficiently simulated classically is central to understanding the boundary between classical and quantum computation. However, polyno…
A Study on Quantum Graph Neural Networks Applied to Molecular Physics
Simone Piperno, Andrea Ceschini, Su Yeon Chang +3
This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approa…
Latent Style-based Quantum GAN for high-quality Image Generation
Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux +2
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images…
Symmetry breaking in geometric quantum machine learning in the presence of noise
Cenk Tüysüz, Su Yeon Chang, Maria Demidik +3
Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouragi…
Approximately Equivariant Quantum Neural Network for Group Symmetries in Images
Su Yeon Chang, Michele Grossi, Bertrand Le Saux +1
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of n…
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
Su Yeon Chang, Edwin Agnew, Elías F. Combarro +3
In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a…
Impact of quantum noise on the training of quantum Generative Adversarial Networks
Kerstin Borras, Su Yeon Chang, Lena Funcke +8
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 e…
Quantum computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
Dynamical localization simulated on actual quantum hardware
Andrea Pizzamiglio, Su Yeon Chang, Maria Bondani +3
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 qua…
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Su Yeon Chang, Steven Herbert, Sofia Vallecorsa +2
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 cer…
Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
Su Yeon Chang, Sofia Vallecorsa, Elías F. Combarro +1
Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelera…
Bell Diagonal and Werner state generation: entanglement, non-locality, steering and discord on the IBM quantum computer
Elias Riedel Gårding, Nicolas Schwaller, Su Yeon Chang +8
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 test…