Variance of Relative Surprisal as Single-Shot Quantifier

The variance of (relative) surprisal, also known as varentropy, so far mostly plays a role in information theory as quantifying the leading-order corrections to asymptotic independent and identically distributed (IID) limits. Here, we comprehensively study the use of it to derive single-shot results in (quantum) information theory. We show that it gives genuine sufficient and necessary conditions for approximate state transitions between pairs of quantum states in the single-shot setting, without the need for further optimization. We also clarify its relation to smoothed min and max entropies, and construct a monotone for resource theories using only the standard (relative) entropy and variance of (relative) surprisal. This immediately gives rise to enhanced lower bounds for entropy production in random processes. We establish certain properties of the variance of relative surprisal, which will be useful for further investigations, such as uniform continuity and upper bounds on the violation of subadditivity. Motivated by our results, we further derive a simple and physically appealing axiomatic single-shot characterization of (relative) entropy, which we believe to be of independent interest. We illustrate our results with several applications, ranging from interconvertibility of ergodic states, over Landauer erasure to a bound on the necessary dimension of the catalyst for catalytic state transitions and Boltzmann's H theorem.

Automated summary

The study comprehensively explores the application of variance of surprisal in (quantum) information theory and finds that it can be used to derive genuine approximate state transition conditions in the single-shot setting. It also clarifies its relation to entropy and proposes a monotone for resource theories. Certain properties of the variance of surprisal are determined, which are useful for further investigations.