5 papers
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…
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…
Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
Junggu Choi, Tak Hur, Daniel K. Park +4
Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study e…