387 citations · 1.3k across the 27 of their papers we have counts for
5 papers · 2 filters
Quantum readout error mitigation via deep learning
Jihye Kim, Byungdu Oh, Yonuk Chong +2
Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols…
Configurable sublinear circuits for quantum state preparation
Israel F. Araujo, Daniel K. Park, Teresa B. Ludermir +3
The theory of quantum algorithms promises unprecedented benefits of harnessing the laws of quantum mechanics for solving certain computational problems. A persistent obstacle to us…
Quantum convolutional neural network for classical data classification
Tak Hur, Leeseok Kim, Daniel K. Park
With the rapid advance of quantum machine learning, several proposals for the quantum-analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark fully…
Robust quantum classifier with minimal overhead
Daniel K. Park, Carsten Blank, Francesco Petruccione
To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational co…
Quantum State Discrimination for Supervised Classification
Roberto Giuntini, Hector Freytes, Daniel K. Park +4
In this paper we investigate the connection between quantum information theory and machine learning. In particular, we show how quantum state discrimination can represent a useful…