Quantum Ensemble Learning with Quantum Support Vector Machine

Ensemble Learning, a technique that combines multiple base models to enhance performance and reduce variance, is a well-established approach in machine learning. In recent years, quantum machine learning has emerged as a field aiming to accelerate classical machine learning through quantum algorithms. In this paper, we introduce a quantum ensemble learning framework, with a particular focus on utilizing the parallel amplitude amplification (parallel AA) technique when employing a quantum support vector machine (QSVM) as the base learner. This approach reduces complexity compared to classical ensemble techniques, making it a promising advancement in the field of quantum machine learning. It demonstrates significant improvements in time complexity and maintains competitive accuracy when compared to classical bagging, including support vector machine (SVM) and least squares support vector machine (LS-SVM).