## Almost Ramanujan Expanders from Arbitrary Expanders via Operator Amplification

We give an efficient algorithm that (locally) transforms any bounded degree expander graph into another that is almost optimal. As an applicaton, one can obtain almost optimal explicit constructions of quantum expanders, dimension expanders, monotone expanders, etc..

Abstract BibTeX
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

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

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.

*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.