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arXiv · 2206.04300

Cone-Restricted Information Theory

Abstract

The max-relative entropy and the conditional min-entropy it induces have become central to one-shot information theory. Both may be expressed in terms of a conic program over the positive semidefinite cone. Recently, it was shown that the same conic program altered to be over the separable cone admits an operational interpretation in terms of communicating classical information over a quantum channel. In this work, we generalize this framework of replacing the cone to determine which results in quantum information theory rely upon the positive semidefinite cone and which can be generalized. We show the fully quantum Stein's lemma and asymptotic equipartition property break down if the cone exponentially increases in resourcefulness but never approximates the positive semidefinite cone. However, we show for CQ states, the separable cone is sufficient to recover the asymptotic theory, thereby drawing a strong distinction between the fully and partial quantum settings. We present parallel results for the extended conditional min-entropy. In doing so, we extend the notion of k-superpositive channels to superchannels. We also present operational uses of this framework. We first show the cone restricted min-entropy of a Choi operator captures a measure of entanglement-assisted noiseless classical communication using restricted measurements. We show that quantum majorization results naturally generalize to other cones. As a novel example, we introduce a new min-entropy-like quantity that captures the quantum majorization of quantum channels in terms of bistochastic pre-processing. Lastly, we relate this framework to general conic norms and their non-additivity. Throughout this work we emphasize the introduced measures' relationship to general convex resource theories. In particular, we look at both resource theories that capture locality and resource theories of coherence/Abelian symmetries.

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BibTeXRIS

Ian George, Eric Chitambar. 2022-06-09. Cone-Restricted Information Theory. https://doi.org/10.1088/1751-8121%2Fad52d5

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