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Record W2161734848 · doi:10.1142/s2010326312500025

THE ABSOLUTE POSITIVE PARTIAL TRANSPOSE PROPERTY FOR RANDOM INDUCED STATES

2012· article· en· W2161734848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRandom Matrices Theory and Application · 2012
Typearticle
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
Fundersnot available
KeywordsMathematicsTransposeDimension (graph theory)Wishart distributionCombinatoricsRandom matrixLimit (mathematics)Algebraic numberState (computer science)Mathematical analysisPhysicsQuantum mechanicsStatisticsAlgorithm

Abstract

fetched live from OpenAlex

In this paper, we first obtain an algebraic formula for the moments of a centered Wishart matrix, and apply it to obtain new convergence results in the large dimension limit when both parameters of the distribution tend to infinity at different speeds. We use this result to investigate APPT (absolute positive partial transpose) quantum states. We show that the threshold for a bipartite random induced state on C d = C d 1 ⊗ C d 2 , obtained by partial tracing a random pure state on C d ⊗ C s , being APPT occurs if the environmental dimension s is of order s 0 = min (d 1 , d 2 ) 3 max (d 1 , d 2 ). That is, when s ≥ Cs 0 , such a random induced state is APPT with large probability, while such a random states is not APPT with large probability when s ≤ cs 0 . Besides, we compute effectively C and c and show that it is possible to replace them by the same sharp transition constant when min (d 1 , d 2 ) 2 ≪ d.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.517
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.298
Teacher spread0.280 · 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