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Quantum annealing of a frustrated magnet

Author

Listed:
  • Yuqian Zhao

    (Huazhong University of Science and Technology)

  • Zhaohua Ma

    (Huazhong University of Science and Technology)

  • Zhangzhen He

    (Chinese Academy of Sciences)

  • Haijun Liao

    (Chinese Academy of Sciences
    Songshan Lake Materials Laboratory)

  • Yan-Cheng Wang

    (Beihang University
    Tianmushan Laboratory)

  • Junfeng Wang

    (Huazhong University of Science and Technology)

  • Yuesheng Li

    (Huazhong University of Science and Technology)

Abstract

Quantum annealing, which involves quantum tunnelling among possible solutions, has state-of-the-art applications not only in quickly finding the lowest-energy configuration of a complex system, but also in quantum computing. Here we report a single-crystal study of the frustrated magnet α-CoV2O6, consisting of a triangular arrangement of ferromagnetic Ising spin chains without evident structural disorder. We observe quantum annealing phenomena resulting from time-reversal symmetry breaking in a tiny transverse field. Below ~ 1 K, the system exhibits no indication of approaching the lowest-energy state for at least 15 hours in zero transverse field, but quickly converges towards that configuration with a nearly temperature-independent relaxation time of ~ 10 seconds in a transverse field of ~ 3.5 mK. Our many-body simulations show qualitative agreement with the experimental results, and suggest that a tiny transverse field can profoundly enhance quantum spin fluctuations, triggering rapid quantum annealing process from topological metastable Kosterlitz-Thouless phases, at low temperatures.

Suggested Citation

  • Yuqian Zhao & Zhaohua Ma & Zhangzhen He & Haijun Liao & Yan-Cheng Wang & Junfeng Wang & Yuesheng Li, 2024. "Quantum annealing of a frustrated magnet," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47819-y
    DOI: 10.1038/s41467-024-47819-y
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    1. J. Zhang & G. Pagano & P. W. Hess & A. Kyprianidis & P. Becker & H. Kaplan & A. V. Gorshkov & Z.-X. Gong & C. Monroe, 2017. "Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator," Nature, Nature, vol. 551(7682), pages 601-604, November.
    2. Andrew D. King & Juan Carrasquilla & Jack Raymond & Isil Ozfidan & Evgeny Andriyash & Andrew Berkley & Mauricio Reis & Trevor Lanting & Richard Harris & Fabio Altomare & Kelly Boothby & Paul I. Bunyk , 2018. "Observation of topological phenomena in a programmable lattice of 1,800 qubits," Nature, Nature, vol. 560(7719), pages 456-460, August.
    3. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    4. Hannes Bernien & Sylvain Schwartz & Alexander Keesling & Harry Levine & Ahmed Omran & Hannes Pichler & Soonwon Choi & Alexander S. Zibrov & Manuel Endres & Markus Greiner & Vladan Vuletić & Mikhail D., 2017. "Probing many-body dynamics on a 51-atom quantum simulator," Nature, Nature, vol. 551(7682), pages 579-584, November.
    5. Henning Labuhn & Daniel Barredo & Sylvain Ravets & Sylvain de Léséleuc & Tommaso Macrì & Thierry Lahaye & Antoine Browaeys, 2016. "Tunable two-dimensional arrays of single Rydberg atoms for realizing quantum Ising models," Nature, Nature, vol. 534(7609), pages 667-670, June.
    6. M. W. Johnson & M. H. S. Amin & S. Gildert & T. Lanting & F. Hamze & N. Dickson & R. Harris & A. J. Berkley & J. Johansson & P. Bunyk & E. M. Chapple & C. Enderud & J. P. Hilton & K. Karimi & E. Ladiz, 2011. "Quantum annealing with manufactured spins," Nature, Nature, vol. 473(7346), pages 194-198, May.
    7. D. M. Silevitch & D. Bitko & J. Brooke & S. Ghosh & G. Aeppli & T. F. Rosenbaum, 2007. "A ferromagnet in a continuously tunable random field," Nature, Nature, vol. 448(7153), pages 567-570, August.
    8. Han Li & Yuan Da Liao & Bin-Bin Chen & Xu-Tao Zeng & Xian-Lei Sheng & Yang Qi & Zi Yang Meng & Wei Li, 2020. "Kosterlitz-Thouless melting of magnetic order in the triangular quantum Ising material TmMgGaO4," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    9. J. Brooke & T. F. Rosenbaum & G. Aeppli, 2001. "Tunable quantum tunnelling of magnetic domain walls," Nature, Nature, vol. 413(6856), pages 610-613, October.
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