English

Smaller Low-Depth Circuits for Kronecker Powers

Data Structures and Algorithms 2022-11-11 v1

Abstract

We give new, smaller constructions of constant-depth linear circuits for computing any matrix which is the Kronecker power of a fixed matrix. A standard argument (e.g., the mixed product property of Kronecker products, or a generalization of the Fast Walsh-Hadamard transform) shows that any such N×NN \times N matrix has a depth-2 circuit of size O(N1.5)O(N^{1.5}). We improve on this for all such matrices, and especially for some such matrices of particular interest: - For any integer q>1q > 1 and any matrix which is the Kronecker power of a fixed q×qq \times q matrix, we construct a depth-2 circuit of size O(N1.5aq)O(N^{1.5 - a_q}), where aq>0a_q > 0 is a positive constant depending only on qq. No bound beating size O(N1.5)O(N^{1.5}) was previously known for any q>2q>2. - For the case q=2q=2, i.e., for any matrix which is the Kronecker power of a fixed 2×22 \times 2 matrix, we construct a depth-2 circuit of size O(N1.446)O(N^{1.446}), improving the prior best size O(N1.493)O(N^{1.493}) [Alman, 2021]. - For the Walsh-Hadamard transform, we construct a depth-2 circuit of size O(N1.443)O(N^{1.443}), improving the prior best size O(N1.476)O(N^{1.476}) [Alman, 2021]. - For the disjointness matrix (the communication matrix of set disjointness, or equivalently, the matrix for the linear transform that evaluates a multilinear polynomial on all 0/10/1 inputs), we construct a depth-2 circuit of size O(N1.258)O(N^{1.258}), improving the prior best size O(N1.272)O(N^{1.272}) [Jukna and Sergeev, 2013]. Our constructions also generalize to improving the standard construction for any depth O(logN)\leq O(\log N). Our main technical tool is an improved way to convert a nontrivial circuit for any matrix into a circuit for its Kronecker powers. Our new bounds provably could not be achieved using the approaches of prior work.

Cite

@article{arxiv.2211.05217,
  title  = {Smaller Low-Depth Circuits for Kronecker Powers},
  author = {Josh Alman and Yunfeng Guan and Ashwin Padaki},
  journal= {arXiv preprint arXiv:2211.05217},
  year   = {2022}
}

Comments

36 pages, to appear in the 34th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2023)

R2 v1 2026-06-28T05:33:19.974Z