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Record W3209048954 · doi:10.1002/andp.202100310

Decoherence of Dirac‐Quantumness for Open Particles in a Dilatonic Black Hole

2021· article· en· W3209048954 on OpenAlexaff
Chengjun Yao, Kai Yan, Quanying Wu, Yin‐Zhong Wu, Xiang Hao

Bibliographic record

VenueAnnalen der Physik · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Electrodynamics and Casimir Effect
Canadian institutionsPacific Institute for the Mathematical SciencesUniversity of British Columbia
FundersSponsoring Consortium for Open Access Publishing in Particle Physics
KeywordsPhysicsQuantum decoherenceQuantum mechanicsQuantum nonlocalityCoherence (philosophical gambling strategy)Quantum discordAmplitude damping channelDilatonQuantumQuantum entanglementQuantum electrodynamics

Abstract

fetched live from OpenAlex

Abstract The quantumness of Dirac particles for quantized fields in a dilatonic black hole is estimated by means of quantum channel. A general Bloch vector representation of quantum channel in black hole spacetimes beyond single mode approximation is developed. The nonclassicality of Dirac particles can be measured by the minimization of quantum coherence over all orthonormal basis sets. The quantumness of the channel decreases as the dilaton field increases. The interplay between the external reservoir noise and dilaton black hole on the dynamical behavior of quantum coherence and steerability is investigated in the Pauli basis. The external environment is modeled by a random telegraph noise channel. The monotonous decay of quantum nonlocality occurs in the weak coupling case. The degradation and revival of quantum nonlocality are observed in the strong coupling condition. It is found that quantum fluctuation effects of the external reservoir can protect quantum coherence and steerability from the information loss of the black hole.

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)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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