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Detecting the Universal Fractional Entropy of Majorana Zero Modes

2019· article· en· W2947586174 on OpenAlexafffund
Eran Sela, Yuval Oreg, Stephan Plugge, Nikolaus Hartman, Silvia Lüscher, Joshua Folk

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of British Columbia
FundersStewart Blusson Quantum Matter Institute, University of British ColumbiaNatural Sciences and Engineering Research Council of CanadaUnited States-Israel Binational Science FoundationCanadian Institute for Advanced ResearchHorizon 2020 Framework ProgrammeDeutsche ForschungsgemeinschaftIsrael Science FoundationCanada Foundation for InnovationMicrosoft
KeywordsMAJORANAPhysicsEntropy (arrow of time)QubitTopological entropyStatistical physicsQuantum mechanicsQuantumMathematicsCombinatoricsFermion

Abstract

fetched live from OpenAlex

A pair of Majorana zero modes (MZMs) constitutes a nonlocal qubit whose entropy is log2. Upon strongly coupling one of the constituent MZMs to a reservoir with a continuous density of states, a universal entropy change of 1/2log2 is expected to be observed across an intermediate temperature plateau. We adapt the entropy-measurement scheme that was the basis of a recent experiment by Hartman et al. [Nat. Phys. 14, 1083 (2018)10.1038/s41567-018-0250-5] to the case of a proximitized topological system hosting MZMs and propose a method to measure this 1/2log2 entropy change-an unambiguous signature of the nonlocal nature of the topological state. This approach offers an experimental strategy to distinguish MZMs from non topological states.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.254
Teacher spread0.243 · 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 designBench or experimental
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

Citations67
Published2019
Admission routes2
Has abstractyes

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