Quantum Inspired Adaptive Boosting

    Building on the quantum ensemble based classifier algorithm of Schuld and Petruccione (2012), we devise equivalent classical algorithms which show that this quantum ensemble method does not have advantage over classical algorithms. One of the classical algorithms is extremely simple and runs in constant time for each classifiable input. 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 are tested and found to be comparable to the AdaBoost algorithm on publicly available data sets.

    Bálint Daróczy, Katalin Friedl, László Kabódi, Attila Pereszlényi, Dániel Szabó
    Proceedings of the 11th Hungarian-Japanese Symposium on Discrete Mathematics and Its Applications, May 27 — 30, 2019, Tokyo, Japan, 2019