33 citations · 48 across the 7 of their papers we have counts for
7 papers
Improved sample complexity bound for sample-based Lindbladian simulation
Siheon Park, Youngjin Seo, Byeongseon Go +3
We establish improved sample-complexity bounds for sample-based Lindbladian simulation based on the Wave Matrix Lindbladization (WML) algorithm. For a jump operator with dimens…
Code-agnostic bosonic noise suppression with hybrid rotations
Saurabh U. Shringarpure, Siheon Park, Sungjoo Cho +4
Physical-level noise on traveling bosonic modes remains a critical bottleneck for scalable quantum information processing. We show that for any single-mode bosonic code (qumode) co…
Ballistic bosonic noise suppression with hybrid qumode-qubit rotation gates
Saurabh U. Shringarpure, Siheon Park, Sungjoo Cho +4
Noise suppression is of paramount importance for reliable quantum information processing and computation. We show that for any single-mode bosonic code (qumode) corrupted by therma…
Sample-based Hamiltonian and Lindbladian simulation: Non-asymptotic analysis of sample complexity
Byeongseon Go, Hyukjoon Kwon, Siheon Park +2
Density matrix exponentiation (DME) is a quantum algorithm that processes multiple copies of a program state to realize the Hamiltonian evolution . Wave matrix Lindb…
Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Changwon Lee, Israel F. Araujo, Dongha Kim +4
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This a…
Quantum Spectral Clustering Framework via Compact Circuit Structures
Hyeong-Gyu Kim, Siheon Park, June-Koo Kevin Rhee
Spectral machine learning methods based on spectral graph theory are powerful but scale poorly due to costly eigen-analysis. Although various eigenvector approximation methods have…
Variational Quantum Approximate Support Vector Machine with Inference Transfer
Siheon Park, Daniel K. Park, June-Koo Kevin Rhee
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variati…