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Cancer classification based on gene expression increases early diagnosis andrecovery, but high-dimensional genes with a small number of samples are a majorchallenge. This work introduces a new hybrid quantum kernel support vectormachine (QKSVM) combined with a Binary Harris hawk optimization (BHHO) basedgene selection for cancer classification on a quantum simulator. This studyaims to improve the microarray cancer prediction performance with the quantumkernel estimation based on the informative genes by BHHO. The feature selectionis a critical step in large-dimensional features, and BHHO is used to selectimportant features. The BHHO mimics the behavior of the cooperative action ofHarris hawks in nature. The principal component analysis (PCA) is applied toreduce the selected genes to match the qubit numbers. After which, the quantumcomputer is used to estimate the kernel with the training data of the reducedgenes and generate the quantum kernel matrix. Moreover, the classical computeris used to draw the support vectors based on the quantum kernel matrix. Also,the prediction stage is performed with the classical device. Finally, theproposed approach is applied to colon and breast microarray datasets andevaluated with all genes and the selected genes by BHHO. The proposed approachis found to enhance the overall performance with two datasets. Also, theproposed approach is evaluated with different quantum feature maps (kernels)and classical kernel (RBF).

An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks