Complexity as information in spin-glass Gibbs states and metastates: upper bounds at nonzero temperature and long-range models
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TL;DR
This paper establishes upper bounds on the complexity of Gibbs states and metastates in spin-glass models with finite-range interactions at positive temperatures, linking complexity to surface area and extending to long-range models.
Contribution
It introduces a new definition of complexity based on mutual information and derives upper bounds for both short- and long-range spin-glass models at positive temperatures.
Findings
Upper bounds on complexity proportional to surface area for short-range models.
Extension of Gibbs state definitions to long-range interactions.
Results valid for a broad class of disorder distributions.
Abstract
In classical finite-range spin systems, especially those with disorder such as spin glasses, a low-temperature Gibbs state may be a mixture of a number of pure or ordered states; the complexity of the Gibbs state has been defined in the past roughly as the logarithm of this number, assuming the question is meaningful in a finite system. As non-trivial mixtures of pure states do not occur in finite size, in a recent paper [Phys. Rev. E 101, 042114 (2020)] H\"oller and the author introduced a definition of the complexity of an infinite-size Gibbs state as the mutual information between the pure state and the spin configuration in a finite region, and applied this also within a metastate construction. (A metastate is a probability distribution on Gibbs states.) They found an upper bound on the complexity for models of Ising spins in which each spin interacts with only a finite number of…
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Taxonomy
TopicsTheoretical and Computational Physics · Complex Systems and Time Series Analysis · Neural Networks and Applications
