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• W3208595835 abstract "Abstract Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. The AdaBoost algorithm is a well-known classical boosting algorithm for weak learners with binary hypotheses. Recently, Arunachalam and Maity presented the first quantum boosting algorithm by quantizing AdaBoost. Their algorithm, which we refer to as QAdaBoost hereafter, is a quantum adaptation of AdaBoost and only works for the binary hypothesis case. QAdaBoost is quadratically faster than AdaBoost in terms of the VC dimension of the hypothesis class of the weak learner. However, QAdaBoost is polynomially worse in the bias of the weak learner. In this work, we address an open question by Izdebski et al. on the existence of boosting algorithms for quantum weak learners with non-binary hypotheses. We answer this question in the affirmative by developing the QRealBoost algorithm motivated by the classical RealBoost algorithm. The main technical challenge was to provide provable guarantees for convergence, generalization bounds, and quantum speedup, given that quantum subroutines are noisy and probabilistic. We prove that QRealBoost retains the quadratic speedup of QAdaBoost over AdaBoost and further achieves a polynomial speedup over QAdaBoost in terms of both the bias of the learner and the time taken by the learner to learn the target concept class. Finally, we perform empirical evaluations on QRealBoost and report encouraging observations on quantum simulators by benchmarking the convergence performance of QRealBoost against QAdaBoost, AdaBoost, and RealBoost on a subset of the MNIST dataset and Breast Cancer Wisconsin dataset." @default.
• W3208595835 created "2021-11-08" @default.
• W3208595835 creator A5003553632 @default.
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• W3208595835 date "2023-03-03" @default.
• W3208595835 modified "2023-09-25" @default.
• W3208595835 title "Quantum Boosting using Domain-Partitioning Hypotheses" @default.
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• W3208595835 doi "https://doi.org/10.21203/rs.3.rs-2633875/v1" @default.
• W3208595835 hasPublicationYear "2023" @default.
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