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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets

Author

Listed:
  • Abhinav Kandala

    (IBM T.J. Watson Research Center)

  • Antonio Mezzacapo

    (IBM T.J. Watson Research Center)

  • Kristan Temme

    (IBM T.J. Watson Research Center)

  • Maika Takita

    (IBM T.J. Watson Research Center)

  • Markus Brink

    (IBM T.J. Watson Research Center)

  • Jerry M. Chow

    (IBM T.J. Watson Research Center)

  • Jay M. Gambetta

    (IBM T.J. Watson Research Center)

Abstract

The ground-state energy of small molecules is determined efficiently using six qubits of a superconducting quantum processor.

Suggested Citation

  • Abhinav Kandala & Antonio Mezzacapo & Kristan Temme & Maika Takita & Markus Brink & Jerry M. Chow & Jay M. Gambetta, 2017. "Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets," Nature, Nature, vol. 549(7671), pages 242-246, September.
    • Handle: RePEc:nat:nature:v:549:y:2017:i:7671:d:10.1038_nature23879
    DOI: 10.1038/nature23879
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    Citations

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    Cited by:

    1. Samson Wang & Enrico Fontana & M. Cerezo & Kunal Sharma & Akira Sone & Lukasz Cincio & Patrick J. Coles, 2021. "Noise-induced barren plateaus in variational quantum algorithms," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Mario E Rivero-Angeles, 2021. "Quantum-based wireless sensor networks: A review and open questions," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    3. He, Zhimin & Deng, Maijie & Zheng, Shenggen & Li, Lvzhou & Situ, Haozhen, 2023. "GSQAS: Graph Self-supervised Quantum Architecture Search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Giacomo Torlai & Christopher J. Wood & Atithi Acharya & Giuseppe Carleo & Juan Carrasquilla & Leandro Aolita, 2023. "Quantum process tomography with unsupervised learning and tensor networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Samuel Fern'andez-Lorenzo & Diego Porras & Juan Jos'e Garc'ia-Ripoll, 2020. "Hybrid quantum-classical optimization for financial index tracking," Papers 2008.12050, arXiv.org, revised Oct 2021.
    6. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    7. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Sitan Chen & Jordan Cotler & Hsin-Yuan Huang & Jerry Li, 2023. "The complexity of NISQ," Nature Communications, Nature, vol. 14(1), pages 1-6, December.
    9. Alexander McCaskey & Eugene Dumitrescu & Mengsu Chen & Dmitry Lyakh & Travis Humble, 2018. "Validating quantum-classical programming models with tensor network simulations," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-19, December.

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