Quantum One-class Classification With a Distance-based Classifier
Abstract
The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.
Cite
@article{arxiv.2007.16200,
title = {Quantum One-class Classification With a Distance-based Classifier},
author = {Nicolas M. de Oliveira and Lucas P. de Albuquerque and Wilson R. de Oliveira and Teresa B. Ludermir and Adenilton J. da Silva},
journal= {arXiv preprint arXiv:2007.16200},
year = {2021}
}
Comments
Accepted for publication in The International Joint Conference on Neural Networks (IJCNN), 2021