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Record W612185544 · doi:10.1103/physrevb.93.201301

Coherence and degree of time-bin entanglement from quantum dots

2016· article· en· W612185544 on OpenAlexfundno aff
Tobias Huber, Laurin Ostermann, Maximilian Prilmüller, Glenn S. Solomon, Helmut Ritsch, Gregor Weihs, Ana Predojević

Bibliographic record

VenuePhysical review. B./Physical review. B · 2016
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsnot available
FundersAustrian Science FundEuropean Research CouncilÖsterreichischen Akademie der WissenschaftenCanadian Institute for Advanced Research
KeywordsDephasingBiexcitonQuantum entanglementCoherence (philosophical gambling strategy)PhysicsConcurrenceExcitationCoherence timeW stateQuantum mechanicsExcitonQuantum dotPhotonMultipartite entanglementQuantumAtomic physicsSquashed entanglement

Abstract

fetched live from OpenAlex

We report a study on coherence of excitation of single quantum dots. We address the coherent excitation of biexcitons, the process that is indispensable for deterministic photon pair generation in quantum dots. Based on theoretical modeling we optimized the duration of the excitation pulse in our experiment to minimize the laser-induced dephasing and increase the biexciton-to-background single-exciton occupation probability. An additional effect of this approach is a high degree of time-bin entanglement with a concurrence of up to 0.78(6) and a 0.88(3) overlap with a maximally entangled state.

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 score0.003

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 science0.0000.001
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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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