12 citations · 19 across the 6 of their papers we have counts for
8 papers
Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
Ju-Young Ryu, Tak Hur, Daniel K. Park
Understanding generalization is essential for assessing the practical viability of quantum machine learning. In many applications, quantum models operate on classical data that mus…
Deployment of Large IQP Circuit Born Machines on Qubit-Limited Hardware Using Noise Injection
Ju-Young Ryu, Wooyeong Song, Kwangil Bae +2
Quantum circuit Born machines (QCBMs) built with instantaneous quantum polynomial (IQP) circuits have loss functions that can be efficiently estimated classically, but require quan…
An approach using geometric diagrams to generic Bell inequalities with multiple observables
Junghee Ryu, Jinhyoung Lee, Hoon Ryu
We extend the generic Bell inequalities suggested by Son, Lee, and Kim [Phys. Rev. Lett. 96, 060406 (2006)] to incorporate multiple observables for tripartite systems and introduce…
Operational Quasiprobability in Quantum Thermodynamics: Work Extraction by Coherence and Non-joint Measurability
Jeongwoo Jae, Junghee Ryu, Hoon Ryu
We employ the operational quasiprobability (OQ) as a work distribution, which reproduces the Jarzynski equality and yields the average work consistent with the classical definition…
Designing generalized elegant Bell inequalities in higher dimensions from a Tsirelson bound
Kwangil Bae, Junghee Ryu, Ilkwon Sohn +1
Elegant Bell inequality is well known for its distinctive property, being maximally violated by maximal entanglement, mutually unbiased bases, and symmetric informationally complet…
Exponentially Enhanced Scheme for the Heralded Qudit GHZ State in Linear Optics
Seungbeom Chin, Junghee Ryu, Yong-Su Kim
High-dimensional multipartite entanglement plays a crucial role in quantum information science. However, existing schemes for generating such entanglement become complex and costly…
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…
On the feasibility of quantum teleportation protocols implemented with Silicon devices
Junghee Ryu, Hoon Ryu
With recent experimental advancements demonstrating high-fidelity universal logic gates and basic programmability, Silicon-based spin quantum bit (qubit) have emerged as promising…