387 citations · 1.3k across the 27 of their papers we have counts for
29 papers
Scalable quantum measurement error mitigation via conditional independence and transfer learning
ChangWon Lee, Daniel K. Park
Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep lea…
Variational quantum state discriminator for supervised machine learning
Dongkeun Lee, Kyunghyun Baek, Joonsuk Huh +1
Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the…
Variational quantum one-class classifier
Gunhee Park, Joonsuk Huh, Daniel K. Park
One-class classification is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm…
Hierarchical quantum circuit representations for neural architecture search
Matt Lourens, Ilya Sinayskiy, Daniel K. Park +2
Machine learning with hierarchical quantum circuits, usually referred to as Quantum Convolutional Neural Networks (QCNNs), is a promising prospect for near-term quantum computing.…
Classical-to-quantum convolutional neural network transfer learning
Juhyeon Kim, Joonsuk Huh, Daniel K. Park
Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained…
Variational Quantum Approximate Support Vector Machine with Inference Transfer
Siheon Park, Daniel K. Park, June-Koo Kevin Rhee
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variati…