# The spherical ensemble and quasi-Monte-Carlo designs

## TL;DR

This paper demonstrates that the spherical ensemble provides nearly optimal quasi-Monte-Carlo integration on the sphere, with high probability, due to a new concentration inequality for the ensemble.

## Contribution

It introduces a new concentration of measure inequality for the spherical ensemble and shows its nearly optimal convergence properties for numerical integration.

## Key findings

- Spherical ensemble points form nearly quasi-Monte-Carlo designs
- High-probability convergence results for numerical integration
- New explicit concentration inequality for the spherical ensemble

## Abstract

The spherical ensemble is a well-known ensemble of N repulsive points on the two-dimensional sphere, which can realized in various ways (as a random matrix ensemble, a determinantal point process, a Coulomb gas, a Quantum Hall state...). Here we show that the spherical ensemble enjoys nearly optimal convergence properties from the point of view of numerical integration. More precisely, it is shown that the numerical integration rule corresponding to N nodes on the two-dimensional sphere sampled in the spherical ensemble is, with overwhelming probability, nearly a quasi-Monte-Carlo design in the sense of Brauchart-Saff-Sloan-Womersley (for any smoothness parameter s less than or equal to two). The key ingredient is a new explicit concentration of measure inequality for the spherical ensemble.