57 citations · 64 across the 2 of their papers we have counts for
5 papers
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…
Quantum-enhanced analysis of discrete stochastic processes
Carsten Blank, Daniel K. Park, Francesco Petruccione
Discrete stochastic processes (DSP) are instrumental for modelling the dynamics of probabilistic systems and have a wide spectrum of applications in science and engineering. DSPs a…
The theory of the quantum kernel-based binary classifier
Daniel K. Park, Carsten Blank, Francesco Petruccione
Binary classification is a fundamental problem in machine learning. Recent development of quantum similarity-based binary classifiers and kernel method that exploit quantum interfe…
Quantum classifier with tailored quantum kernel
Carsten Blank, Daniel K. Park, June-Koo Kevin Rhee +1
Kernel methods have a wide spectrum of applications in machine learning. Recently, a link between quantum computing and kernel theory has been formally established, opening up oppo…