6 papers
Generative Modeling of Quantum Distribution with Functional Flow Matching
Jaehoon Hahm, Tak Hur, Joonseok Lee +1
The emergence of powerful deep generative models based on diffusion and flow matching has enabled the learning and modeling of complex distributions. Learning quantum distributions…
Optimizing Quantum Data Embeddings for Ligand-Based Virtual Screening
Junggu Choi, Tak Hur, Seokhoon Jeong +5
Effective molecular representations are essential for ligand-based virtual screening. We investigate how quantum data embedding strategies can improve this task by developing and e…
Scalable Neural Decoders for Practical Real-Time Quantum Error Correction
Changwon Lee, Tak Hur, Daniel K. Park
Real-time, scalable, and accurate decoding is a critical component for realizing a fault-tolerant quantum computer. While Transformer-based neural decoders such as \textit{AlphaQub…
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…
Understanding Generalization in Quantum Machine Learning with Margins
Tak Hur, Daniel K. Park
Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current gener…