Reply to: Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Link
https://www.nature.com/articles/s41467-023-41108-w.pdf
Reference11 articles.
1. Fan, C. et al. Searching for spin glass ground states through deep reinforcement learning. Nat. Commun. 14, 725 (2023).
2. Wang, W., Machta, J. & Katzgraber, H. G. Comparing monte carlo methods for finding ground states of ising spin glasses: population annealing, simulated annealing, and parallel tempering. Phys. Rev. E 92, 013303 (2015).
3. Pál, K. F. The ground state of the cubic spin glass with short-range interactions of Gaussian distribution. Phys. A: Stat. Mechanics Appl. 233, 60–66 (1996).
4. Pál, K. F. The ground state energy of the Edwards-Anderson Ising spin glass with a hybrid genetic algorithm. Phys. A: Stat. Mechanics Appl. 223, 283–292 (1996).
5. Boettcher, S. Extremal optimization for Sherrington-Kirkpatrick spin glasses. Eur. Phys. J. B: Condens. Matter Complex Syst. 46, 501–505 (2005).
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