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A Novel Multi-Photon Entangled State with Enhanced Resilience to Path Loss

2024· article· en· W4400878874 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNonlinear Optical Materials Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsResilience (materials science)Path (computing)PhotonComputer scienceState (computer science)PhysicsComputer networkQuantum mechanicsAlgorithm

Abstract

fetched live from OpenAlex

In the realm of quantum information, entanglement stands as a cornerstone phenomenon. It underpins a vast array of quantum information processes, offering significant potential for advancements in quantum computing, communication, and sensing. This paper introduces a novel multi-photon entangled state, which generalizes the maximally entangled single-photon state and exhibits remarkable resilience to signal attenuation in photonic applications. We demonstrate the novelty of the proposed state through a simplified target detection model and illustrate its superior performance over traditional single-photon protocols, attributed to its higher entanglement level and enhanced noise suppression capabilities. Our findings suggest that the proposed multi-photon state holds significant promise for enhancing the efficiency and reliability of photonic applications subject to loss. This work lays the groundwork for future exploration into the practical applications of multi-photon entangled states in quantum technologies, potentially revolutionizing our approach to quantum sensing and beyond.

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.

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 score0.174
Threshold uncertainty score0.665

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)0.0000.001

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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