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Record W2030158676 · doi:10.1088/0957-4484/14/1/318

Single-walled carbon nanotube superconductor entangler: noise correlations and Einstein Podolsky Rosen states

2002· article· en· W2030158676 on OpenAlexaff
Vincent Bouchiat, N. M. Chtchelkatchev, D. Feinberg, G. B. Lesovik, Thierry Martin

Bibliographic record

VenueNanotechnology · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversité de Sherbrooke
FundersRussian Science FoundationRussian Foundation for Basic ResearchRussian Science Support Foundation
KeywordsCarbon nanotubeSuperconductivityMaterials scienceGrapheneNanotubeElectronNanowireQuantum dotCondensed matter physicsNoise (video)Carbon nanotube quantum dotNanotechnologyPhysicsAtomic physicsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

We propose a device which implements a solid-state nanostructured electron entangler. It consists of a single-walled carbon nanotube connected at both end to normal state electrodes and coupled in its middle part to a superconducting nanowire. Such a device acts as an electronic beam splitter for correlated electrons originating from the superconductor. We first show that it can be used to detect positive (bosonic--like) noise correlations in an fermionic system. Furthermore, it provides a source for entangled electrons in the two arms of the splitter. For generating entangled electron states, we propose two kinds of setup based either on spin or energy filters. It respectively consists of ferromagnetic pads and of a system of electrostatic gates which define quantum dots. The fabrication of this device would require state-of-the-art nanofabrication techniques, carbon nanotube synthesis and integration, as well as atomic force microscopy imaging and manipulation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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 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

Citations68
Published2002
Admission routes1
Has abstractyes

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