On parity decision trees for Fourier-sparse Boolean functions
Nikhil S. Mande, Swagato Sanyal

TL;DR
This paper investigates parity decision trees for Boolean functions, establishing bounds on their complexity related to Fourier sparsity, and explores implications for the log-rank conjecture and XOR function communication complexity.
Contribution
It proves existence of parity decision trees with depth O(√k) for Fourier-sparse functions using a simple sampling method, and extends results under a folding property conjecture.
Findings
Existence of parity decision trees of depth O(√k) for Fourier-sparse functions.
Naive parity sampling suffices to construct these trees.
A folding property leads to polynomially smaller decision trees and improved bounds on XOR function complexity.
Abstract
We study parity decision trees for Boolean functions. The motivation of our study is the log-rank conjecture for XOR functions and its connection to Fourier analysis and parity decision tree complexity. Let f be a Boolean function with Fourier support S and Fourier sparsity k. 1) We prove via the probabilistic method that there exists a parity decision tree of depth O(sqrt k) that computes f. This matches the best known upper bound on the parity decision tree complexity of Boolean functions (Tsang, Wong, Xie, and Zhang, FOCS 2013). Moreover, while previous constructions (Tsang et al., FOCS 2013, Shpilka, Tal, and Volk, Comput. Complex. 2017) build the trees by carefully choosing the parities to be queried in each step, our proof shows that a naive sampling of the parities suffices. 2) We generalize the above result by showing that if the Fourier spectra of Boolean functions satisfy…
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