  The data encoding circuits used in quantum support vector machine (QSVM)
kernels play a crucial role in their classification accuracy. However, manually
designing these circuits poses significant challenges in terms of time and
performance. To address this, we leverage the GASP (Genetic Algorithm for State
Preparation) framework for gate sequence selection in QSVM kernel circuits. We
explore supervised and unsupervised kernel loss functions' impact on encoding
circuit optimisation and evaluate them on diverse datasets for binary and
multiple-class scenarios. Benchmarking against classical and quantum kernels
reveals GA-generated circuits matching or surpassing standard techniques. We
analyse the relationship between test accuracy and quantum kernel entropy, with
results indicating a positive correlation. Our automated framework reduces
trial and error, and enables improved QSVM based machine learning performance
for finance, healthcare, and materials science applications.
