387 citations · 1.3k across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Linear-depth quantum circuits for multiqubit controlled gates
Adenilton J. da Silva, Daniel K. Park
Quantum circuit depth minimization is critical for practical applications of circuit-based quantum computation. In this work, we present a systematic procedure to decompose multiqu…
Compact quantum kernel-based binary classifier
Carsten Blank, Adenilton J. da Silva, Lucas P. de Albuquerque +2
Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers c…