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Record W3209044694 · doi:10.1103/physreva.105.022433

Quantumness beyond entanglement: The case of symmetric states

2022· preprint· en· W3209044694 on OpenAlexafffund
Aaron Z. Goldberg, Markus Grassl, Gerd Leuchs, L. L. Sánchez-Soto

Bibliographic record

VenuePhysical review. A/Physical review, A · 2022
Typepreprint
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsNational Research Council CanadaUniversity of Toronto
FundersMegagrantsMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaHorizon 2020MitacsMinistry of Education and Science of the Russian FederationNatural Sciences and Engineering Research Council of CanadaFundacja na rzecz Nauki PolskiejEuropean Commission
KeywordsQuantum entanglementSeparable stateUnitary stateUnitary transformationState (computer science)Quantum stateMAJORANAMeasure (data warehouse)Multipartite entanglementSeparable spaceQuantum mechanicsTransformation (genetics)PhysicsModalQuantumTheoretical physicsSquashed entanglementMathematicsQuantum discordComputer scienceLawMathematical analysisPolitical scienceAlgorithm

Abstract

fetched live from OpenAlex

Nowadays, it is accepted that truly quantum correlations can exist even in the absence of entanglement. For the case of symmetric states, a physically trivial unitary transformation can alter a state from entangled to separable, and vice versa. We propose to certify the presence of quantumness via an average of a state's bipartite entanglement properties over all physically relevant modal decompositions. We investigate extremal states for such a measure: SU(2)-coherent states possess the least quantumness, whereas the opposite extreme is inhabited by states with maximally spread Majorana constellations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.363
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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