$\alpha$QBoost: An Iteratively Weighted Adiabatic Trained Classifier

TL;DR

$QBoost is a novel adiabatically-trained ensemble classifier that improves performance, stability, and convergence speed, making it suitable for applications requiring explainability and rapid inference.

Contribution

It introduces an iteratively weighted adiabatic training method that enhances ensemble stability and efficiency over classical approaches.

Findings

Higher accuracy on unseen data

More stable with fewer classifiers

Faster convergence compared to previous methods

Abstract

A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance, but also more stability with less classifiers, an attribute that is critically important in areas like explainability and speed-of-inference. In all, the empirical analysis displays that the algorithm can provide an increase in performance on unseen data by strengthening stability of the statistical model through further minimizing and balancing variance and bias, while decreasing the time to convergence over its predecessors.