Error mitigation for quantum kernel based machine learning methods on
  IonQ and IBM quantum computers
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
Sasan Moradi Christoph Brandner Macauley Coggins Robert Wille Wolfgang Drexler Laszlo Papp