  Expressibility is a crucial factor of a Parameterized Quantum Circuit (PQC).
In the context of Variational Quantum Algorithms (VQA) based Quantum Machine
Learning (QML), a QML model composed of highly expressible PQC and sufficient
number of qubits is theoretically capable of approximating any arbitrary
continuous function. While much research has explored the relationship between
expressibility and learning performance, as well as the number of layers in
PQCs, the connection between expressibility and PQC structure has received
comparatively less attention. In this paper, we analyze the connection between
expressibility and the types of quantum gates within PQCs using a Gradient
Boosting Tree model and SHapley Additive exPlanations (SHAP) values. Our
analysis is performed on 1,615 instances of PQC derived from 19 PQC topologies,
each with 2-18 qubits and 1-5 layers. The findings of our analysis provide
guidance for designing highly expressible PQCs, suggesting the integration of
more RX or RY gates while maintaining a careful balance with the number of CNOT
gates. Furthermore, our evaluation offers an additional evidence of
expressibility saturation, as observed by previous studies.
