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Record W2806955586 · doi:10.1038/s41598-018-20507-w

Chaos and dynamical complexity in the quantum to classical transition

2018· article· en· W2806955586 on OpenAlexaff
Bibek Pokharel, Moses Misplon, Walter Lynn, Peter Duggins, Kevin Hallman, Dustin Anderson, Arie Kapulkin, Arjendu K. Pattanayak

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsThornhill Medical (Canada)
FundersKavli Institute for Theoretical Physics, University of California, Santa BarbaraUniversità degli Studi dell'InsubriaHoward Hughes Medical Institute
KeywordsCHAOS (operating system)Statistical physicsQuantumTransition (genetics)Quantum chaosComputer sciencePhysicsQuantum mechanicsQuantum dynamicsBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract We study the largest Lyapunov exponents λ and dynamical complexity for an open quantum driven double-well oscillator, mapping its dependence on coupling to the environment Γ as well as effective Planck’s constant β 2 . We show that in general λ increases with effective Hilbert space size (as β decreases, or the system becomes larger and closer to the classical limit). However, if the classical limit is regular, there is always a quantum system with λ greater than the classical λ , with several examples where the quantum system is chaotic even though the classical system is regular. While the quantum chaotic attractors are generally of the same family as the classical attractors, we also find quantum attractors with no classical counterpart. Contrary to the standard wisdom, the correspondence limit can thus be the most difficult to achieve for certain classically chaotic systems. These phenomena occur in experimentally accessible regimes.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.276
Teacher spread0.249 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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