Effective eigenvalue approximation from moments for self-adjoint trace-class operators

TL;DR

This paper introduces a method to approximate the spectrum of self-adjoint trace-class operators using moments, achieving super-exponential convergence and providing effective bounds without diagonalization, with applications in quantum physics.

Contribution

The authors develop a moment-based spectral approximation method for self-adjoint trace-class operators, offering super-exponential convergence and practical bounds for eigenvalues.

Findings

Converges to the spectrum in Hausdorff metric using moments.

Super-exponential convergence to extremal eigenvalues.

Provides effective lower bounds for the minimal eigenvalue.

Abstract

Spectral properties of bounded linear operators play a crucial role in several areas of mathematics and physics. For each self-adjoint, trace-class operator $O$ we define a set $\Lambda_n\subset \mathbb{R}$, and we show that it converges to the spectrum of $O$ in the Hausdorff metric under mild conditions. Our set $\Lambda_n$ only depends on the first $n$ moments of $O$. We show that it can be effectively calculated for physically relevant operators, and it approximates the spectrum well without diagonalization. We prove that using the above method we can converge to the minimal and maximal eigenvalues with super-exponential speed. We also construct monotone increasing lower bounds $q_n$ for the minimal eigenvalue (or decreasing upper bounds for the maximal eigenvalue). This sequence only depends on the moments of $O$ and a concrete upper estimate of its $1$-norm; we also demonstrate that $q_n$ can be effectively calculated for a large class of physically relevant operators. This rigorous lower bound $q_n$ tends to the minimal eigenvalue with super-exponential speed provided that $O$ is not positive semidefinite. As a by-product, we obtain computable upper bounds for the $1$-norm of $O$, too. Numerical examples demonstrate the relevance of our approximation in estimating entropy and negativity, which is useful, among others, in quantum optical and in open quantum system models. The results can be directly applicable to problems in quantum information, statistical mechanics, and quantum thermodynamics, where using traditional techniques based on diagonalization is impractical.