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arXiv · 2609.39802

Double Localization for Quantum Gibbs Sampler Gaps: From an Abstract Framework to Finite-Group Models

Ryu Hayakawa·Angus Southwell·Caesnan M. G. Leditto·Kuo-Chin Chen·Min-Hsiu Hsieh
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Abstract

We introduce double localization, a framework for proving spectral gaps of quantum Gibbs samplers through two complementary operations: geometric localization, which selects updates supported in small spatial regions, and interface localization, which focuses on a model-defined subspace of observables while remaining geometrically global. Our abstract gap theorem combines a global bound on this subspace, called the interface, with local quantum gap estimates under quantitative coupling and geometric assembly conditions. The resulting explicit lower bound controls the gap of the full dynamics without requiring the interface to be invariant under the generator. This separation allows global estimates, including classical comparison, to be combined with local control of the remaining quantum directions. We apply the framework to Hamiltonians built from the vertex terms of Kitaev's finite-group quantum double construction, without plaquette terms, on finite simple three-regular graphs of girth at least six. For the specified local Davies dynamics, we prove an unconditional spectral-gap lower bound by a positive constant independent of graph size for every fixed nontrivial finite group and each fixed inverse temperature $β$ with $0\leβJ<\log(5/3)$, where $J>0$ is the coupling strength. For the smallest non-Abelian group $S_3$, double localization yields a positive lower bound uniform in both graph size and the entire interval $0\leβJ\le\log 3$, providing a guarantee that does not follow directly from existing results. This demonstrates how model-specific finite calculations can extend spectral-gap guarantees for the full quantum dynamics.

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Ryu Hayakawa, Angus Southwell, Caesnan M. G. Leditto, Kuo-Chin Chen, Min-Hsiu Hsieh. 2026-09-30. Double Localization for Quantum Gibbs Sampler Gaps: From an Abstract Framework to Finite-Group Models. https://arxiv.org/abs/2609.39802

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