Characterizing scalable measures of quantum resources
Fernando Parisio

TL;DR
This paper introduces a method to analyze how quantum resource measures like entanglement scale with multiple copies, using self-similarity and recursive relations, enabling efficient estimation of resources in large quantum systems.
Contribution
It proposes a scalable framework for quantum resource measures based on self-similarity, allowing determination of resource quantities for large tensor powers without extensive computations.
Findings
One-shot distillable entanglement can be accurately estimated for large tensor powers.
Fibonacci polynomials characterize the functional form of certain resource measures.
Superactivation of non-additivity can occur in the context of scalable quantum resources.
Abstract
The question of how quantities, like entanglement and coherence, depend on the number of copies of a given state is addressed. This is a hard problem, often involving optimizations over Hilbert spaces of large dimensions. Here, we propose a way to circumvent the direct evaluation of such quantities, provided that the employed measures satisfy a self-similarity property. We say that a quantity is {\it scalable} if it can be described as a function of the variables for , while, preserving the tensor-product structure. If analyticity is assumed, recursive relations can be derived for the Maclaurin series of , which enable us to determine its possible functional forms (in terms of the mentioned variables). In particular, we find that if ${\cal…
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