Thinning, photonic beamsplitting, and a general discrete entropy power inequality
Saikat Guha, Jeffrey H. Shapiro, Raul Garcia-Patron Sanchez

TL;DR
This paper develops an axiomatic framework for a natural discrete-variable entropy power inequality (EPI), drawing parallels with the continuous case and exploring its implications in quantum channel capacity.
Contribution
It introduces a discrete-variable EPI based on geometric distributions and a discrete scaled addition, linking it to quantum information theory and prior work on ultra-log-concave distributions.
Findings
Proposes a discrete EPI with geometric distribution as maximum entropy.
Defines entropy power as the mean of a geometric random variable.
Shows the discrete EPI holds when entropy power is redefined as e^{H(X)}.
Abstract
Many partially-successful attempts have been made to find the most natural discrete-variable version of Shannon's entropy power inequality (EPI). We develop an axiomatic framework from which we deduce the natural form of a discrete-variable EPI and an associated entropic monotonicity in a discrete-variable central limit theorem. In this discrete EPI, the geometric distribution, which has the maximum entropy among all discrete distributions with a given mean, assumes a role analogous to the Gaussian distribution in Shannon's EPI. The entropy power of is defined as the mean of a geometric random variable with entropy . The crux of our construction is a discrete-variable version of Lieb's scaled addition of two discrete random variables and with . We discuss the relationship of our discrete EPI with recent work of Yu and Johnson who…
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