Quantum Boosting using Domain-Partitioning Hypotheses

10/25/2021
by   Debajyoti Bera, et al.
0

Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. Freund and Schapire gave the first classical boosting algorithm for binary hypothesis known as AdaBoost, and this was recently adapted into a quantum boosting algorithm by Arunachalam et al. Their quantum boosting algorithm (which we refer to as Q-AdaBoost) is quadratically faster than the classical version in terms of the VC-dimension of the hypothesis class of the weak learner but polynomially worse in the bias of the weak learner. In this work we design a different quantum boosting algorithm that uses domain partitioning hypotheses that are significantly more flexible than those used in prior quantum boosting algorithms in terms of margin calculations. Our algorithm Q-RealBoost is inspired by the "Real AdaBoost" (aka. RealBoost) extension to the original AdaBoost algorithm. Further, we show that Q-RealBoost provides a polynomial speedup over Q-AdaBoost in terms of both the bias of the weak learner and the time taken by the weak learner to learn the target concept class.

READ FULL TEXT

page 1

page 5

page 23

page 25

research
09/17/2020

Improved Quantum Boosting

Boosting is a general method to convert a weak learner (which generates ...
research
10/01/2022

Efficient Quantum Agnostic Improper Learning of Decision Trees

The agnostic setting is the hardest generalization of the PAC model sinc...
research
05/26/2015

Some Open Problems in Optimal AdaBoost and Decision Stumps

The significance of the study of the theoretical and practical propertie...
research
01/31/2020

Boosting Simple Learners

We consider boosting algorithms under the restriction that the weak lear...
research
02/01/2021

Quantum Inspired Adaptive Boosting

Building on the quantum ensemble based classifier algorithm of Schuld an...
research
02/12/2020

Quantum Boosting

Suppose we have a weak learning algorithm A for a Boolean-valued problem...
research
01/27/2023

AdaBoost is not an Optimal Weak to Strong Learner

AdaBoost is a classic boosting algorithm for combining multiple inaccura...

Please sign up or login with your details

Forgot password? Click here to reset