Quantum Boosting using Domain-Partitioning Hypotheses. (arXiv:2110.12793v3 [quant-ph] UPDATED)

Boosting is an ensemble learning method that converts a weak learner into a
strong learner in the PAC learning framework. Freund and Schapire designed the
Godel prize-winning algorithm named AdaBoost that can boost learners, which
output binary hypotheses. Recently, Arunachalam and Maity presented the first
quantum boosting algorithm with similar theoretical guarantees. Their
algorithm, which we refer to as QAdaBoost henceforth, is a quantum adaptation
of AdaBoost and only works for the binary hypothesis case. QAdaBoost is
quadratically faster than …

arxiv boosting partitioning quantum