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Record W1998541997 · doi:10.1103/physreva.77.033628

Tunable quantum-fluctuation-controlled coherent spin dynamics

2008· article· en· W1998541997 on OpenAlexaff
Jun Song, Fei Zhou

Bibliographic record

VenuePhysical Review A · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of British Columbia
FundersKavli Institute for Theoretical Physics, University of California, Santa Barbara
KeywordsPhysicsQuantum fluctuationQuantumQuantum dynamicsThermal fluctuationsUltracold atomQuantum phasesZeeman effectOptical latticeSpin (aerodynamics)Quantum mechanicsCoherence (philosophical gambling strategy)ScalingCondensed matter physicsQuantum phase transitionMagnetic field

Abstract

fetched live from OpenAlex

Temporal evolution of a macroscopic condensate of ultracold atoms is usually driven by mean-field potentials, either due to scattering between atoms or due to coupling to external fields; and coherent quantum dynamics of this type have been observed in various cold atom experiments. In this paper, we report results of studies of a class of quantum spin dynamics which are purely driven by zero point quantum fluctuations of spin collective coordinates. Unlike the usual mean-field coherent dynamics, quantum-fluctuation-controlled spin dynamics (or QFCSD) studied here are very sensitive to variation of quantum fluctuations and the corresponding driving potentials induced by zero point motions can be tuned by four to five orders of magnitude using optical lattices. These dynamics have unique dependence on optical lattice potential depths and quadratic Zeeman fields. We also find that thermal fluctuations generally can further enhance the induced potentials although the enhancement in deep optical lattices is much less substantial than in traps or shallow lattices. QFCSD can be potentially used to calibrate quantum fluctuations and investigate correlated fluctuations and various universal scaling properties near quantum critical points.

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 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)0.000
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: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 teacher head, 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

Citations10
Published2008
Admission routes1
Has abstractyes

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