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We present and compare two methods of generating quantum feature maps forquantum-enhanced support vector machine, a classifier based on kernel method,by which we can access high dimensional Hilbert space efficiently. The firstmethod is a genetic algorithm with multi-objective fitness function usingpenalty method, which incorporates maximizing the accuracy of classificationand minimizing the gate cost of quantum feature map circuit. The second methoduses variational quantum circuit, focusing on how to contruct the ansatz basedon unitary matrix decomposition. Numerical results and comparisons arepresented to demonstrate how the fitness fuction reduces gate cost whileremaining high accuracy and conducting circuit through unitary matrix obtainseven better performance. In particular, we propose some thoughts on reducingand optimizing the gate cost of a circuit while remaining perfect accuracy.

Generating quantum feature maps for SVM classifier