Enhanced Quantum Ensemble Classification Algorithm With Shallow Circuit and Parallelism Mechanism

ABSTRACT Ensemble learning plays a central role in improving the generalization of machine learning models, yet its full potential remains underexplored in the quantum domain. In this work, we propose a Quantum Ensemble Learning (QEL) algorithm that leverages the parallelism of quantum computing to construct diverse classifiers in a shallow‐circuit ensemble. The method uses a control‐qubit‐based sampling strategy to create superpositions of training subsamples, enabling simultaneous evaluation of multiple weak learners. The algorithm proceeds through four stages: quantum state preparation, superposition‐based sampling, learning via quantum interference, and final measurement. Our framework is flexible and allows different quantum classifiers to be used as base learners. We demonstrate this by implementing a robust QSVM‐based ensemble with quantum feature extraction using PauliFeatureMaps. Unlike classical methods, this technique adds classifiers to time complexity additively rather than multiplicatively due to the parallelism of quantum computing. To demonstrate the workflow of the proposed algorithm, we include a step‐by‐step example using the XOR problem. Simulation results further validate the effectiveness of the QEL framework—enhanced by the integration of a Quantum Support Vector Machine (QSVM) classifier with quantum feature extraction—showing improved classification accuracy compared to existing algorithms across several UCI benchmark datasets using IBM Qiskit.