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Interferometric Probes of Many-Body Localization

2014· article· en· W2109984092 on OpenAlexafffund
Maksym Serbyn, Michael Knap, Sarang Gopalakrishnan, Zlatko Papić, Norman Y. Yao, Chris R. Laumann, Dmitry A. Abanin, M. D. Lukin, Eugene Demler

Bibliographic record

VenuePhysical Review Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum many-body systems
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaCenter for Ultracold Atoms, Massachusetts Institute of TechnologyAir Force Office of Scientific ResearchDefense Advanced Research Projects AgencyAustrian Science FundGovernment of CanadaHarvard UniversityMultidisciplinary University Research InitiativeInstitut Périmètre de physique théoriquePrinceton University
KeywordsDephasingSpinsPhysicsDelocalized electronQuantum entanglementInterferometryPhase (matter)Spin (aerodynamics)Atom interferometerQuantum mechanicsPulse (music)Condensed matter physicsStatistical physicsQuantumAstronomical interferometer

Abstract

fetched live from OpenAlex

We propose a method for detecting many-body localization (MBL) in disordered spin systems. The method involves pulsed coherent spin manipulations that probe the dephasing of a given spin due to its entanglement with a set of distant spins. It allows one to distinguish the MBL phase from a noninteracting localized phase and a delocalized phase. In particular, we show that for a properly chosen pulse sequence the MBL phase exhibits a characteristic power-law decay reflecting its slow growth of entanglement. We find that this power-law decay is robust with respect to thermal and disorder averaging, provide numerical simulations supporting our results, and discuss possible experimental realizations in solid-state and cold-atom systems.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.271
Teacher spread0.261 · 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

Citations174
Published2014
Admission routes2
Has abstractyes

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