Publications (9)
Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
Tak Hur, Israel F. Araujo, Daniel K. Park
Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this stu…
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…
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…
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…
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…