8 citations · 11 across the 6 of their papers we have counts for
6 papers
Generative Quantum Data Embeddings for Supervised Learning
Jaewoong Heo, Daniel K. Park
Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are embedded into quantum states.…
Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
Chanyoung Kim, Myeonghwan Seong, Yujin Kim +2
Partial differential equations (PDEs) are central to modeling physical and engineering systems, but repeatedly solving parametric PDEs remains computationally expensive. Operator l…
Multi-channel convolutional neural quantum embedding
Yujin Kim, Changjae Im, Taehyun Kim +2
Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational…
Neural quantum embedding via deterministic quantum computation with one qubit
Hongfeng Liu, Tak Hur, Shitao Zhang +10
Quantum computing is expected to provide exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as quantum data embedding, to m…
Expressivity of deterministic quantum computation with one qubit
Yujin Kim, Daniel K. Park
Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its su…
Distributed quantum machine learning via classical communication
Kiwmann Hwang, Hyang-Tag Lim, Yong-Su Kim +2
Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantu…