Searcharxiv⌕ Search

arXiv · 2610.27154

Exact spectral integrability of a finite-channel random Schrödinger operator: from fermionization of the Dyson--Riccati gas to Kontsevich integral

Abstract

We solve exactly, for arbitrary finite channel number $N$, the Riccati dynamics of a quasi-one-dimensional random matrix Schrödinger operator with Hermitian white-noise potential in the $β=2$ unitary symmetry class. The associated Dyson--Riccati eigenvalue gas admits an exact free-fermion representation at any sample length $L$, which in the limit $L\to \infty$ expresses the density of states and the total Lyapunov growth rate exactly through a single Airy Hankel $τ$-function providing multichannel generalization of $N=1$ Halperin--Thouless formulas. The $τ-$function further admits an outgoing-contour Kontsevich matrix Airy integral representation. Its saddle-point and double-scaling analyses give, in a unified way, the finite-channel density Lifshitz tail, and the large-$N$ spectral edge. In particular, for $N\gg 1$ the sharp $N=\infty$ edge is softened within an $N^{-4/5}$ spectral window, with its profile governed by the tritronquée solution of Painlevé I. We conjecture universality of this quasi-one-dimensional scaling edge profile for a broad class of $β=2$ random band matrices.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yan V. Fyodorov, Pierre Le Doussal. 2026-10-19. Exact spectral integrability of a finite-channel random Schrödinger operator: from fermionization of the Dyson--Riccati gas to Kontsevich integral. https://arxiv.org/abs/2610.27154

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Embedding stochastic dynamics in latent manifolds of recurrent neural networks

Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits through their ability to support rich time-dependent behaviour. Whilst high-dimensional by construction, they can be used to encode low-dimensional dynamical systems, for example by restricting to low-rank connectivity, hence mirroring the emergence of latent neural manifolds found in empirical neural data. Here, we exploit a relationship between RNNs and a class of universal approximators to train networks that can represent arbitrary, nonlinear stochastic differential equations (SDEs), with given drift and diffusion coefficients, within a low-dimensional latent subspace. We show that RNNs can faithfully embed the drift and diffusion of a given SDE, including nonlinear and nonequilibrium dynamics such as chaotic attractors. We apply this approach by constructing RNN realisations of stochastic systems that transiently explore various attractors through both input-driven switching and autonomous transitions driven by nonequilibrium currents, which we interpret as models of associative and sequential (episodic) memory. To elucidate how these dynamics are encoded in the network, we introduce decompositions of the RNN based on its asymmetric connectivity and its time-irreversibility. Our results show that neural populations can implement a broad class of stochastic dynamics within low-dimensional manifolds, unifying ideas from the dynamics of neural computation, the theory of neural manifolds, associative memory, and nonequilibrium statistical mechanics.

cond-mat.dis-nn↗

Deep-layered machines have a built-in Occam's razor

Many complex systems map vast numbers of microscopic descriptions onto far fewer macroscopic outcomes. Surprisingly, these input-output maps strongly favor simple outputs. In an exactly solvable deep-layered machine, we prove that simplicity bias strengthens with every layer. The probability of an output function $F$ approaches $\Pr(F)\propto 2^{-K(F)}$, where $K$ is the Kolmogorov complexity. Our results show that hierarchical composition alone can generate an intrinsic Occam's razor, suggesting a general mechanism for simplicity bias in learning and evolution.

cond-mat.dis-nn↗

Cup and Cap Topological Neural Network

Topological Deep Learning (TDL) is designed to learn features associated not only with nodes but also with edges, triangles and higher dimensional simplices. However, most TDL approaches, being based on boundary operators and Hodge Laplacians, have the limitation that features and signals cannot be lifted or lowered across more than one dimension per layer. To overcome this limitation, in this work, we propose the adoption of the cup and cap products. Specifically, we formulate the Cup and Cap Topological Neural Network (CCNN), a topological deep learning architecture designed to learn node-based variables (or 0-cochains) by taking into account their many-body interactions (e.g. triangles) present in the data. We validate CCNN by assessing its performance on the TopoBench datasets, demonstrating its competitive performance with respect to other simplicial complex neural networks.

cond-mat.dis-nn↗