The Role of Connectivity and Scale in Variational Quantum Classifiers (VQC): An Empirical Analysis of ZFeatureMap-EfficientSU2 on Standard Classification Datasets

Abstract

This study presents a comparative empirical evaluation of Variational Quantum Classifier (VQC) performance within the Quantum Machine Learning (QML) framework, focusing on VQC architectural analysis under NISQ-era qubit constraints. The VQC was designed utilizing ZFeatureMap for data encoding and EfficientSU2 as the ansatz. A systematic test was performed to evaluate the impact of three entanglement topologies (default, circular, pairwise) and two qubit scales (4 and 8) with four classical optimizers on standard classification datasets: Iris, Wine, and Breast Cancer. The results demonstrated that no single configuration is universally optimal. The default topology achieved the highest peak accuracy on multi-class datasets (Iris: 97% with P-BFGS), suggesting its efficiency in simpler parameter landscapes. Conversely, the pairwise and circular topologies showed superior stability and competitive accuracy (up to 92%) on the high-dimensional Breast Cancer dataset, confirming that richer qubit connectivity is essential for effective feature separation in more complex classification problems. Increasing the scale from 4 to 8 qubits was found to be crucial for improving the overall stability and consistency of performance rather than merely increasing peak accuracy. These findings provide essential empirical guidance for designing optimal and trainable VQC architectures under current quantum device limitations.