Oleksii Malyshev | Searcharxiv

arXiv2026

Linear response across interaction regimes in two-dimensional ferromagnets

Recent discoveries of two-dimensional (2D) ferromagnets have stimulated intense interest in understanding and controlling their spin transport properties. A central microscopic feature of these systems is that exchange-driven magnon--magnon interactions are strongly momentum dependent: low-momentum magnons interact weakly, while high-momentum ones can scatter strongly and exhibit collective hydrodynamic behavior. Understanding transport in such systems therefore requires a microscopic description capable of capturing ballistic and hydrodynamic regimes on equal footing. The natural framework is the quantum Boltzmann equation (QBE), whose solution is notoriously difficult because of the multidimensional collision integrals. Here, we develop a method based on an efficient representation of distribution functions as sums of Gaussians, which renders the collision integrals tractable. This approach enables accurate solution of the linearized QBE and computation of momentum- and frequency-resolved linear response in 2D ferromagnets across a broad range of temperatures and magnetic fields. In particular, we resolve a temperature-driven crossover from a ballistic regime dominated by weakly interacting low-momentum magnons to a collective hydrodynamic regime governed by strongly interacting high-momentum modes. Applying this method to monolayer CrCl$_3$, we obtain good agreement with recent nitrogen-vacancy-center dephasing experiments that reported anomalous magnetic noise consistent with magnon sound. More broadly, our work establishes a general framework for computing momentum- and frequency-resolved linear response in interacting 2D quantum systems describable within quantum Boltzmann kinetics.

cond-mat.stat-mech↗

arXiv2026

Distance learning from projective measurements as an information-geometric probe of many-body physics

The ability of modern quantum simulators--both digital and analogue--to generate large ensembles of single-shot projective "snapshots" has opened a data-rich avenue for the study of quantum many-body systems. Unsupervised machine learning analysis of such snapshots has gained traction, with numerous works reconstructing phase diagrams by learning and clustering low-dimensional representations of quantum states. Here, we forgo such representation learning in favour of distance learning: we infer the pairwise distances between quantum states--already sufficient for clustering--directly from snapshots. Specifically, we use a single neural discriminator to estimate Csiszar f-divergences--statistical distances between distributions--in an unsupervised manner. The resulting clusters reveal regimes with different dominant correlations, often coinciding with, but not limited to, conventionally defined phases of matter. Beyond phase-diagram exploration, we connect the infinitesimal limit of the inferred divergences to the Fisher information metric and analyse its finite-size scaling. This yields critical exponents of the discovered transitions and enables snapshot-based analysis of universality classes. We apply distance learning to a diverse set of systems characterised by conventional local order parameters (1D transverse-field and 2D classical Ising models), non-local topological order (extended toric code), and higher-order correlations (fermionic t-J model on a triangular lattice). In all cases, we correctly recover boundaries between distinct correlation regimes and, where applicable, quantitatively match established critical behaviour. Finally, we show that distances to suitably chosen reference snapshot distributions help identify the dominant correlations within the discovered clusters, positioning distance learning as a versatile information-geometric probe of quantum many-body physics.

quant-ph↗