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Record W2010819837 · doi:10.1088/1367-2630/15/1/015004

Entanglement in gapless resonating-valence-bond states

2013· article· en· W2010819837 on OpenAlexaff
Jean-Marie Stéphan, Hyejin Ju, Paul Fendley, Roger G. Melko

Bibliographic record

VenueNew Journal of Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum many-body systems
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsPhysicsGapless playbackQuantum entanglementValence bond theorySpinsQuantum mechanicsInvariant (physics)Square latticeGround stateEntropy (arrow of time)Mathematical physicsCondensed matter physicsQuantumElectron

Abstract

fetched live from OpenAlex

We study resonating-valence-bond (RVB) states on the square lattice of spins and of dimers, as well as SU ( N )-invariant states that interpolate between the two. These states are ground states of gapless models, although the SU (2)-invariant spin RVB state is also believed to be a gapped liquid in its spinful sector. We show that the gapless behavior in spin and dimer RVB states is qualitatively similar by studying the Rényi entropy for splitting a torus into two cylinders. We compute this exactly for dimers, showing it behaves similarly to the familiar one-dimensional log term, although not identically. We extend the exact computation to an effective theory believed to interpolate among these states. By numerical calculations for the SU (2) RVB state and its SU ( N )-invariant generalizations, we provide further support for this belief. We also show how the entanglement entropy behaves qualitatively differently for different values of the Rényi index n , with large values of n proving a more sensitive probe here, by virtue of exhibiting a striking even/odd effect.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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