  Cancer classification based on gene expression increases early diagnosis and
recovery, but high-dimensional genes with a small number of samples are a major
challenge. This work introduces a new hybrid quantum kernel support vector
machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO) based
gene selection for cancer classification on a quantum simulator. This study
aims to improve the microarray cancer prediction performance with the quantum
kernel estimation based on the informative genes by BHHO. The feature selection
is a critical step in large-dimensional features, and BHHO is used to select
important features. The BHHO mimics the behavior of the cooperative action of
Harris hawks in nature. The principal component analysis (PCA) is applied to
reduce the selected genes to match the qubit numbers. After which, the quantum
computer is used to estimate the kernel with the training data of the reduced
genes and generate the quantum kernel matrix. Moreover, the classical computer
is used to draw the support vectors based on the quantum kernel matrix. Also,
the prediction stage is performed with the classical device. Finally, the
proposed approach is applied to colon and breast microarray datasets and
evaluated with all genes and the selected genes by BHHO. The proposed approach
is found to enhance the overall performance with two datasets. Also, the
proposed approach is evaluated with different quantum feature maps (kernels)
and classical kernel (RBF).
