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Record W3107962991 · doi:10.1364/ol.401735

Experimental quantum entanglement and teleportation by tuning remote spatial indistinguishability of independent photons

2020· article· en· W3107962991 on OpenAlexaff
Kai Sun, Yan Wang, Zhenghao Liu, Xiao-Ye Xu, Jin‐Shi Xu, Chuan‐Feng Li, Guang‐Can Guo, Alessia Castellini, Farzam Nosrati, G. Compagno, Rosario Lo Franco

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsQuantum entanglementTeleportationPhotonPhysicsQuantum teleportationQuantum mechanicsPhoton entanglementQuantumMeasure (data warehouse)Polarization (electrochemistry)Quantum channelStatistical physicsComputer science

Abstract

fetched live from OpenAlex

Quantitative control of spatial indistinguishability of identical subsystems as a direct quantum resource at distant sites has not yet been experimentally proven. We design a setup capable of tuning remote spatial indistinguishability of two independent photons by individually adjusting their spatial distribution in two distant regions, leading to polarization entanglement from uncorrelated photons. This is achieved by spatially localized operations and classical communication on photons that meet only at the detectors. The amount of entanglement depends uniquely on the degree of spatial indistinguishability, quantified by an entropic measure <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">I</mml:mi> </mml:mrow> </mml:math> , which enables teleportation with fidelities above the classical threshold. The results open the way to viable indistinguishability-enhanced quantum information processing.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.410

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)0.0000.000

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 head0.230
Teacher spread0.217 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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