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Improved Quantum Boosting

Authors Adam Izdebski, Ronald de Wolf

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Author Details

Adam Izdebski
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Ronald de Wolf
  • QuSoft, CWI and University of Amsterdam, The Netherlands

Funding

  • de Wolf, Ronald: Partially supported by the Dutch Research Council (NWO/OCW), as part of the Quantum Software Consortium programme (project number 024.003.037), and through QuantERA ERA-NET Cofund project QuantAlgo (680-91-034).

Acknowledgements

We thank Srinivasan Arunachalam for many helpful comments, Min-Hsiu Hsieh for sending us an updated version of [Ximing Wang et al., 2021] and answering some questions about this paper, Yassine Hamoudi for answering a question about [Yassine Hamoudi et al., 2020], and the anonymous referees for some helpful pointers.

Cite AsGet BibTex

Adam Izdebski and Ronald de Wolf. Improved Quantum Boosting. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ESA.2023.64

Abstract

Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random). Recently, Arunachalam and Maity [Srinivasan Arunachalam and Reevu Maity, 2020] gave the first quantum improvement for boosting, by combining Freund and Schapire’s AdaBoost algorithm with a quantum algorithm for approximate counting. Their booster is faster than classical boosting as a function of the VC-dimension of the weak learner’s hypothesis class, but worse as a function of the quality of the weak learner. In this paper we give a substantially faster and simpler quantum boosting algorithm, based on Servedio’s SmoothBoost algorithm [Servedio, 2003].

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum computation theory
  • Computing methodologies → Machine learning
Keywords
  • Learning theory
  • Boosting algorithms
  • Quantum computing

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