Characteristic polynomials of tensors via Grassmann integrals and distributions of roots for random Gaussian tensors
Nicolas Delporte, Giacomo La Scala, Naoki Sasakura, Reiko Toriumi
TL;DR
The paper introduces a novel notion of tensor characteristic polynomials defined via a Grassmann partition function, applicable to antisymmetric tensors and extendable to broad permutation symmetries. By averaging over Gaussian tensors and applying a large-$N$ saddle-point analysis, the authors show that the polynomial is monic of degree $N$, its roots form a finite spectrum, and their density converges to a Fuss-Catalan distribution; zeros also relate to tensor eigenvalue analogs and connect to Gurau's generalized Wigner semicircle law through a simple variable transformation. A key result is the equivalence between the generating function of root powers and the partition function's derivative, linking spectral data to tensor invariants and to the combinatorics of Fuss-Catalan numbers. Overall, the work provides a new spectral framework for tensors that reveals universal random-matrix-like structures and suggests avenues for applying Grassmann methods to tensor decompositions and quantum-inspired tensor models.
Abstract
We propose a new definition of characteristic polynomials of tensors based on a partition function of Grassmann variables. This new notion of characteristic polynomial addresses general tensors including totally antisymmetric ones, but not totally symmetric ones. Drawing an analogy with matrix eigenvalues obtained from the roots of their characteristic polynomials, we study the roots of our tensor characteristic polynomial. Unlike standard definitions of eigenvalues of tensors of dimension $N$ giving $\sim e^{\text{constant} \, N}$ number of eigenvalues, our polynomial always has $N$ roots. For random Gaussian tensors, the density of roots follows a generalized Wigner semi-circle law based on the Fuss-Catalan distribution, introduced previously by Gurau [arXiv:2004.02660 [math-ph]].
