Quantified Derandomization of Linear Threshold Circuits
Roei Tell

TL;DR
This paper advances the understanding of derandomization in constant-depth circuits by providing the first quantified derandomization algorithms for $TC^0$ circuits with depth greater than 2, and explores implications for complexity class separations.
Contribution
It introduces the first quantified derandomization algorithm for $TC^0$ circuits with super-linear wires and analyzes how improvements could impact broader complexity class separations.
Findings
Developed a nearly polynomial-time algorithm distinguishing certain $TC^0$ circuit behaviors.
Extended derandomization results to linear threshold circuits, stronger than majority gates.
Showed that modest improvements could lead to breakthroughs in derandomization and complexity class separations.
Abstract
One of the prominent current challenges in complexity theory is the attempt to prove lower bounds for , the class of constant-depth, polynomial-size circuits with majority gates. Relying on the results of Williams (2013), an appealing approach to prove such lower bounds is to construct a non-trivial derandomization algorithm for . In this work we take a first step towards the latter goal, by proving the first positive results regarding the derandomization of circuits of depth . Our first main result is a quantified derandomization algorithm for circuits with a super-linear number of wires. Specifically, we construct an algorithm that gets as input a circuit over input bits with depth and wires, runs in almost-polynomial-time, and distinguishes between the case that rejects at most inputs and the…
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