Challenges and opportunities in quantum optimization

Open Access
Authors
  • A. Abbas
  • A. Ambainis
  • B. Augustino
  • A. Bärtschi
  • H. Buhrman
  • C. Coffrin
  • G. Cortiana
  • V. Dunjko
  • D.J. Egger
  • B.G. Elmegreen
  • N. Franco
  • F. Fratini
  • B. Fuller
  • J. Gacon
  • C. Gonciulea
  • S. Gribling
  • S. Gupta
  • S. Hadfield
  • R. Heese
  • G. Kircher
  • T. Kleinert
  • T. Koch
  • G. Korpas
  • S. Lenk
  • J. Marecek
  • V. Markov
  • G. Mazzola
  • S. Mensa
  • N. Mohseni
  • G. Nannicini
  • C. O’Meara
  • E. Peña Tapia
  • S. Pokutta
  • M. Proissl
  • P. Rebentrost
  • E. Sahin
  • B.C.B. Symons
  • S. Tornow
  • V. Valls
  • S. Woerner
  • M.L. Wolf-Bauwens
  • J. Yard
  • S. Yarkoni
  • D. Zechiel
  • S. Zhuk
  • C. Zoufal
Publication date 12-2024
Journal Nature Reviews Physics
Volume | Issue number 6 | 12
Pages (from-to) 718-735
Number of pages 18
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
  • Faculty of Science (FNWI) - Institute of Physics (IoP) - Institute for Theoretical Physics Amsterdam (ITFA)
Abstract

Quantum computing is advancing rapidly, and quantum optimization is a promising area of application. Quantum optimization algorithms — whether provably exact, provably approximate or heuristic — offer opportunities to demonstrate quantum advantage. Systematic benchmarking is crucial to guide research, track progress and further advance understanding of quantum optimization. Theoretical research and empirical research using real hardware can complement each other, in the move towards demonstrating quantum advantage.
Document type Review article
Note With supplementary file. - Longer version available at ArXiv.org.
Language English
Published at https://doi.org/10.1038/s42254-024-00770-9 https://doi.org/10.48550/arXiv.2312.02279
Other links https://www.scopus.com/pages/publications/85207968009
Downloads
2312.02279v3 (Other version)
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