Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers
Abstract
Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts
Cite
@article{arxiv.2206.01573,
title = {Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers},
author = {Sasan Moradi and Christoph Brandner and Macauley Coggins and Robert Wille and Wolfgang Drexler and Laszlo Papp},
journal= {arXiv preprint arXiv:2206.01573},
year = {2022}
}
Comments
The result of paper is for IBMQ Belem Computer with old technology