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Record W2294399804 · doi:10.1103/physreva.94.033817

Physical resources for optical phase estimation

2016· article· en· W2294399804 on OpenAlexafffund
Jaspreet Sahota, Nicolás Quesada, Daniel F. V. James

Bibliographic record

VenuePhysical review. A/Physical review, A · 2016
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversité de SherbrookeUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum metrologyQuantum entanglementHeisenberg limitQuantum limitQuantumScalingPhysicsQuantum discordStatistical physicsQuantum sensorQuantum mechanicsUncertainty principleQuantum noiseCoherence (philosophical gambling strategy)Squashed entanglementAmplitude damping channelMathematicsQuantum network

Abstract

fetched live from OpenAlex

We study the role of quantum entanglement (particle entanglement and mode entanglement) in optical phase estimation by employing the mode description and the particle description of bosonic probe states. To determine the physical resources responsible for Heisenberg scaling (attainable only in the noiseless case), we restrict our analysis to noiseless quantum-limited optical phase estimation. The quantum Fisher information (QFI) is expressed as a function of the first- and second-order optical coherence functions. The resulting form of the QFI elucidates the deriving metrological resources for quantum phase estimation: field intensity and photon detection correlations. Our analysis confirms that mode entanglement is not required for quantum-enhanced interferometry, whereas particle entanglement is a necessary requirement. Furthermore, the derived forms of the QFI equations are summations of two terms: the classical (shot-noise-limit scaling) term and the quantum (Heisenberg scaling) term. This allows us to clearly identify the physical resources responsible for quantum enhancement in optical phase estimation.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.380
Teacher spread0.361 · 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 designTheoretical or conceptual
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

Citations18
Published2016
Admission routes2
Has abstractyes

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