  Quantum machine learning (QML) represents a promising frontier in the quantum
technologies. In this pursuit of quantum advantage, the quantum kernel method
for support vector machine has emerged as a powerful approach. Entanglement, a
fundamental concept in quantum mechanics, assumes a central role in quantum
computing. In this paper, we investigate the optimal number of entanglement
gates in the quantum kernel feature maps by a multi-objective genetic
algorithm. We distinct the fitness functions of genetic algorithm for non-local
gates for entanglement and local gates to gain insights into the benefits of
employing entanglement gates. Our experiments reveal that the optimal
configuration of quantum circuits for the quantum kernel method incorporates a
proportional number of non-local gates for entanglement. The result complements
the prior literature on quantum kernel generation where non-local gates were
largely suppressed. Furthermore, we demonstrate that the separability indexes
of data can be leveraged to estimate the number of non-local gates required for
the quantum support vector machine's feature maps. This insight can be helpful
in selecting appropriate parameters, such as the entanglement parameter, in
various quantum programming packages like https://qiskit.org/ based on data
analysis. Our findings offer valuable guidance for enhancing the efficiency and
accuracy of quantum machine learning algorithms.
