Evidence map›Paper›PMID 39103383›Full record

ArticleScientific reports2024

Entanglement detection with classical deep neural networks.

Julio Ureña, Antonio Sojo, Juani Bermejo-Vega, Daniel Manzano

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Julio UreñaInstituto de Física Corpuscular (IFIC), CSIC and Universitat de València, Valencia, 46980, Spain.
Antonio SojoElectromagnetism and Matter Physics Department, University of Granada, 18071, Granada, Spain.
Juani Bermejo-VegaElectromagnetism and Matter Physics Department, University of Granada, 18071, Granada, Spain.
Daniel ManzanoElectromagnetism and Matter Physics Department, University of Granada, 18071, Granada, Spain. manzano@onsager.ugr.es.

Funding

Junta de Andalucía A.FQM.752.UGR20Ministerio de Ciencia, Innovación y Universidades PID2021-128970OA- I00
6 · The paper itself

Abstract

In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over

Identifiers

PMID39103383
PMCPMC11300872

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.