This work develops a quantum–statistical framework for analyzing non-local linear kernels by embedding them as Gibbs states of self-adjoint Hamiltonians on a tensor-product Hilbert space. In the symmetric setting, the construction yields a well-defined equilibrium ensemble and separates exact operator-level identities from conditional diagnostics that depend on explicit spectral assumptions. In the classical limit, the conditional Gibbs marginals recover row-wise softmax weights by construction, while a finite kinetic scale introduces a controlled within-block deformation that preserves block structure. Independent of equilibrium considerations, any kernel defines an operator-induced bipartite state whose reduced spectra are determined by the singular values of the kernel. The paper formulates a quantitative purity flatness condition under which the associated operator entanglement approaches the maximal logarithmic scaling allowed by the natural bipartition. Thermodynamic observables are linked to information geometry by identifying the Fisher–Rao metric along the inverse-temperature direction with the Hamiltonian variance, motivating energetic susceptibility and finite-size scaling diagnostics for large operator families. The framework distinguishes operator-state diagnostics, which are well defined for arbitrary kernels, from equilibrium and spectral diagnostics, which require a Hermitian representative, and provides a set of mathematically explicit tools for studying non-local kernels within quantum statistical mechanics. This is a preprint version.
Tamal Dutta Chowdhury (Mon,) studied this question.