Almost Ramanujan Expanders from Arbitrary Expanders via Operator Amplification

TL;DR

This paper presents an efficient, local algorithm to transform any bounded degree expander graph into an almost Ramanujan expander, achieving near-optimal spectral expansion and degree trade-offs, with broad applications in graph theory and quantum computing.

Contribution

It introduces a novel spectral amplification technique extending Ta-Shma's method to matrices, enabling explicit construction of almost Ramanujan expanders from arbitrary expanders.

Findings

Achieves near-quadratic spectral expansion-degree trade-off

Transforms arbitrary expanders into almost Ramanujan expanders efficiently

Enables explicit constructions in quantum and dimension expanders

Abstract

We give an efficient algorithm that transforms any bounded degree expander graph into another that achieves almost optimal (namely, near-quadratic, $d \leq 1/\lambda^{2+o(1)}$) trade-off between (any desired) spectral expansion $\lambda$ and degree $d$. Furthermore, the algorithm is local: every vertex can compute its new neighbors as a subset of its original neighborhood of radius $O(\log(1/\lambda))$. The optimal quadratic trade-off is known as the Ramanujan bound, so our construction gives almost Ramanujan expanders from arbitrary expanders. The locality of the transformation preserves structural properties of the original graph, and thus has many consequences. Applied to Cayley graphs, our transformation shows that any expanding finite group has almost Ramanujan expanding generators. Similarly, one can obtain almost optimal explicit constructions of quantum expanders, dimension expanders, monotone expanders, etc., from existing (suboptimal) constructions of such objects. Another consequence is a "derandomized" random walk on the original (suboptimal) expander with almost optimal convergence rate. Our transformation also applies when the degree is not bounded or the expansion is not constant. We obtain our results by a generalization of Ta-Shma's technique in his breakthrough paper [STOC 2017], used to obtain explicit almost optimal binary codes. Specifically, our spectral amplification extends Ta-Shma's analysis of bias amplification from scalars to matrices of arbitrary dimension in a very natural way. Curiously, while Ta-Shma's explicit bias amplification derandomizes a well-known probabilistic argument (underlying the Gilbert--Varshamov bound), there seems to be no known probabilistic (or other existential) way of achieving our explicit ("high-dimensional") spectral amplification.