# Maximum and minimum entropy states yielding local continuity bounds

## TL;DR

This paper constructs states with extremal entropy within a quantum state's neighborhood, leading to refined local continuity bounds for von Neumann and other entropies, with broad implications for quantum information theory.

## Contribution

It provides explicit constructions for maximum and minimum entropy states in a neighborhood, extending local continuity bounds to various entropy measures using convex optimization techniques.

## Key findings

- Derived states optimize entropy within an ε-ball around a given state
- Established local continuity bounds for von Neumann, Rényi, min-, and max-entropies
- Provided a general condition for maximizing concave functions in an ε-ball

## Abstract

Given an arbitrary quantum state ($\sigma$), we obtain an explicit construction of a state $\rho^*_\varepsilon(\sigma)$ (resp. $\rho_{*,\varepsilon}(\sigma)$) which has the maximum (resp. minimum) entropy among all states which lie in a specified neighbourhood ($\varepsilon$-ball) of $\sigma$. Computing the entropy of these states leads to a local strengthening of the continuity bound of the von Neumann entropy, i.e., the Audenaert-Fannes inequality. Our bound is local in the sense that it depends on the spectrum of $\sigma$. The states $\rho^*_\varepsilon(\sigma)$ and $\rho_{*,\varepsilon}(\sigma)$ depend only on the geometry of the $\varepsilon$-ball and are in fact optimizers for a larger class of entropies. These include the R\'enyi entropy and the min- and max- entropies. This allows us to obtain local continuity bounds for these quantities as well. In obtaining this bound, we first derive a more general result which may be of independent interest, namely a necessary and sufficient condition under which a state maximizes a concave and G\^ateaux-differentiable function in an $\varepsilon$-ball around a given state $\sigma$. Examples of such a function include the von Neumann entropy, and the conditional entropy of bipartite states. Our proofs employ tools from the theory of convex optimization under non-differentiable constraints, in particular Fermat's Rule, and majorization theory.