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Reusability report: Comparing gradient descent and Monte Carlo tree search optimization of quantum annealing schedules

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The Original Article was published on 14 March 2022

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Fig. 1: Fidelity at the end of the quantum annealing process versus annealing time T for 3-SAT instances with n = 11 variables and M = 5 frequency components in the annealing schedule s(t).
Fig. 2: Effect of the number of frequency components M on the algorithm’s performance.
Fig. 3: Fidelity at the end of the annealing process versus T for unweighted MAX CUT problems on 3-regular graphs with n = 12 vertices.

Data availability

The data for reproducing this work are available at https://github.com/condensedAI/quantumzero (ref. 15).

Code availability

The code can be found at https://github.com/condensedAI/quantumzero (ref. 15).

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Acknowledgements

M.W. was supported by the Villum Foundation (research grant no. 25310). This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 847523 ‘INTERACTIONS’.

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Authors and Affiliations

  1. Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

    Matteo M. Wauters & Evert van Nieuwenburg

  2. Lorentz Institute and Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands

    Evert van Nieuwenburg

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  1. Matteo M. Wauters
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  2. Evert van Nieuwenburg
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MMW performed the simulations and analyzed the data. Both authors contributed to writing the code and interpreting the data and to the writing.

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Correspondence to Evert van Nieuwenburg.

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Wauters, M.M., van Nieuwenburg, E. Reusability report: Comparing gradient descent and Monte Carlo tree search optimization of quantum annealing schedules. Nat Mach Intell 4, 810–813 (2022). https://doi.org/10.1038/s42256-022-00535-y

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