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Record W3112092832 · doi:10.1103/physrevd.107.014506

Non-Abelian gauge invariance from dynamical decoupling

2023· article· en· W3112092832 on OpenAlexaff
Valentin Kasper, Torsten V. Zache, Fred Jendrzejewski, Maciej Lewenstein, Erez Zohar

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversité de Sherbrooke
FundersH2020 Marie Skłodowska-Curie ActionsH2020 European Research CouncilAgencia Estatal de InvestigaciónFUNDACIÓ Privada MIR-PUIGFundación CellexEuropean Regional Development FundBarcelona Supercomputing CenterGeneralitat de CatalunyaNarodowe Centrum NaukiAgència de Gestió d'Ajuts Universitaris i de RecercaIsrael Science FoundationHorizon 2020 Framework ProgrammeDeutsche ForschungsgemeinschaftSimons Foundation
KeywordsLattice gauge theoryPhysicsQuantum gauge theoryHamiltonian lattice gauge theoryIntroduction to gauge theorySupersymmetric gauge theoryGauge theoryGauge fixingGauge anomalyTheoretical physicsLattice field theoryAbelian groupBRST quantizationGauge bosonQuantum mechanicsMathematicsPure mathematics

Abstract

fetched live from OpenAlex

Lattice gauge theories are fundamental to such distinct fields as particle physics, condensed matter or quantum information theory. The recent progress in the control of artificial quantum systems already allows for studying Abelian lattice gauge theories in tabletop experiments. However, the realization of non-Abelian models remains challenging. Here, we employ a coherent quantum control scheme to enforce non-Abelian gauge invariance, and discuss this idea in detail for a one-dimensional SU(2) lattice gauge system. We comment on how to extend our scheme to other non-Abelian gauge symmetries and higher spatial dimensions. Because of its wide applicability, the presented coherent control scheme provides a promising route for the quantum simulation of non-Abelian lattice gauge theories.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.007

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 head0.016
GPT teacher head0.396
Teacher spread0.380 · 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.

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

Citations36
Published2023
Admission routes1
Has abstractyes

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