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Direct estimation of functionals of density operators by local operations and classical communication

Quantum Physics · Submitted 18 Apr 2003

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present a method of determining important properties of a shared bipartite quantum state, within the ``distant labs'' paradigm, using \emph{only} local operations and classical communication (LOCC).

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arXiv IDquant-ph/0304123 (version 1)
Pith IDU5MN5Z6X, Pith's permanent record of this paper and its review
CitationArtur K. Ekert, Carolina Moura Alves, Daniel K. L. Oi, L. C. Kwek, Pawel Horodecki. "Direct estimation of functionals of density operators by local operations and classical communication." arXiv:quant-ph/0304123 (2003). https://pith.science/paper/U5MN5Z6X
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,934 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.19 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a method of determining important properties of a shared bipartite quantum state, within the ``distant labs'' paradigm, using \emph{only} local operations and classical communication (LOCC). We apply this procedure to spectrum estimation of shared states, and locally implementable structural physical approximations to incompletely positive maps. This procedure can also be applied to the estimation of channel capacity and measures of entanglement.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a method of determining important properties of a shared bipartite quantum state, within the ``distant labs'' paradigm, using \emph{only} local operations and classical communication (LOCC). contribution 0.99 0.93 0.62 asserted 1.00
s1 We apply this procedure to spectrum estimation of shared states, and locally implementable structural physical approximations to incompletely positive maps. contribution 0.90 0.88 0.12 asserted 1.00
s2 This procedure can also be applied to the estimation of channel capacity and measures of entanglement. contribution 0.70 0.59 0.13 hedged possibility 0.80

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.92.

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not examined
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  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a method of determining important properties of a shared bipartite quantum state, within the ``distant labs'' paradigm, using \emph{only} local operations and classical communication (LOCC). We apply this procedure to spectrum estimation of shared states, and locally implementable structural physical approximations to incompletely positive maps. This procedure can also be applied to the estimation of channel capacity and measures of entanglement.

Human review
References
How the literature uses it

1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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