Strongly convergent matrix models for $q$-Gaussian algebras

Abstract

We construct strongly convergent finite-dimensional random matrix models for finite $q$-Gaussian family in the range $\vert q \vert < \sqrt{2}-1$. The construction has two stages. First, we show that normalized sums of graph-product semicirculars over an Erdos-Renyi graph satisfy the $q$-Toeplitz relations up to an operator norm error converging to zero in probability. Using ultraproduct methods, this yields complete strong convergence, uniformly over all matrix coefficient dimensions, for noncommutative polynomials with bounded degree. Second, we use a quantitative tensor-GUE for graph-product semicirculars to convert these operator models into finite-dimensional random matrices. For every fixed polynomial degree, the convergence is uniform over coefficient dimensions that may be larger than the dimension of the random matrices. As applications, in the above range of $q$, the C$^\ast$-algebra generated by a finite $q$-Gaussian family is MF, and the Brown-Douglas-Fillmore extension semigroup of the nontrivial C$^\ast$-algebra generated by a finite $q$-Gaussian family is not a group.