Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 158 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 29 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 80 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 436 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Properties of computational entanglement measures (2509.21988v1)

Published 26 Sep 2025 in quant-ph, math-ph, and math.MP

Abstract: Quantum entanglement is a useful resource for implementing communication tasks. However, for the resource to be useful in practice, it needs to be accessible by parties with bounded computational resources. Computational entanglement measures quantify the usefulness of entanglement in the presence of limited computational resources. In this paper, we analyze systematically some basic properties of two recently introduced computational entanglement measures, the computational distillable entanglement and entanglement cost. To do so, we introduce lower bound and upper bound extensions of basic properties to address the case when entanglement measures are not defined by a scalar value but when only lower or upper function bounds are available. In particular, we investigate the lower bound convexity and upper bound concavity properties of such measures, and the upper and lower bound additivity with respect to the tensor product. We also observe that these measures are not invariant with local unitaries, although invariance is recovered for efficient unitaries. As a consequence, we obtain that these measures are only LOCC monotones under efficient families of LOCC channels. Our analysis covers both the one-shot scenario and the uniform setting, with properties established for the former naturally extending to the latter.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 0 likes.

Upgrade to Pro to view all of the tweets about this paper: