Noise Quantification and Control in Circuits via Strong Data-Processing Inequalities

TL;DR

This paper investigates the use of strong data-processing inequalities to analyze noise limits in noisy circuits, introducing new bounds and generalizations for majority gates to improve reliable computation.

Contribution

It develops a unified framework for noise bounds in circuits using SDPI, introduces generalized majority gate analysis, and simplifies existing proofs with new insights.

Findings

Lower bounds on circuit depth for reliable computation

Upper bounds on noise levels for 3-majority gates

Generalized noise thresholds for majority gates of any order

Abstract

This essay explores strong data-processing inequalities (SPDI's) as they appear in the work of Evans and Schulman \cite{ES} and von Neumann \cite{vN} on computing with noisy circuits. We first develop the framework in \cite{ES}, which leads to lower bounds on depth and upper bounds on noise that permit reliable computation. We then introduce the $3$-majority gate, introduced by \cite{vN} for the purpose of controlling noise, and obtain an upper bound on noise necessary for its function. We end by generalizing von Neumann's analysis to majority gates of any order, proving an analogous noise threshold and giving a sufficient upper bound for order given a desired level of reliability. The presentation of material has been modified in a way deemed more natural by the author, occasionally leading to simplifications of existing proofs. Furthermore, many computations omitted from the original works have been worked out, and some new commentary added. The intended audience has a rudimentary understanding of information theory similar to that of the author.