Quantum Inspired Adaptive Boosting

TL;DR

This paper demonstrates that a quantum ensemble classifier can be equivalently implemented classically, and introduces quantum-inspired adaptive boosting methods that perform comparably to AdaBoost on standard datasets.

Contribution

It provides classical algorithms equivalent to a quantum ensemble method and introduces quantum-inspired adaptive boosting techniques.

Findings

Classical algorithms can replicate quantum ensemble classifiers.

Quantum-inspired boosting methods perform similarly to AdaBoost.

Simple classical classifier runs in constant time per input.

Abstract

Building on the quantum ensemble based classifier algorithm of Schuld and Petruccione [arXiv:1704.02146v1], we devise equivalent classical algorithms which show that this quantum ensemble method does not have advantage over classical algorithms. Essentially, we simplify their algorithm until it is intuitive to come up with an equivalent classical version. One of the classical algorithms is extremely simple and runs in constant time for each input to be classified. We further develop the idea and, as the main contribution of the paper, we propose methods inspired by combining the quantum ensemble method with adaptive boosting. The algorithms were tested and found to be comparable to the AdaBoost algorithm on publicly available data sets.