Analysis of Parameterized Quantum Circuits: On the Connection Between Expressibility and Types of Quantum Gates

Expressibility is a crucial factor of a parameterized quantum circuit (PQC). In the context of variational-quantum-algorithm-based quantum machine learning (QML), a QML model composed of a highly expressible PQC and a 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 article, we analyze the connection between expressibility and the types of quantum gates within PQCs using a gradient boosting tree model and Shapley additive explanations values. Our analysis is performed on 1615 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 X-rotation or Y-rotation 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.