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Record W4367175926 · doi:10.1116/5.0137579

Obtaining a single-photon weak value from experiments using a strong (many-photon) coherent state

2023· article· en· W4367175926 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAVS Quantum Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of TorontoCanadian Institute for Advanced Research
FundersCentre of Excellence for Quantum Computation and Communication Technology, Australian Research CouncilUniversity of TorontoGriffith UniversityFetzer InstituteCanadian Institute for Advanced ResearchNatural Sciences and Engineering Research Council of CanadaJohn E. Fetzer Memorial Trust
KeywordsPhotonValue (mathematics)PhysicsState (computer science)Avalanche photodiodeFunction (biology)DetectorStatistical physicsQuantum mechanicsOpticsComputer scienceMathematicsStatisticsAlgorithm

Abstract

fetched live from OpenAlex

A common type of weak-value experiment prepares a single particle in one state, weakly measures the occupation number of another state, and post-selects on finding the particle in a third state (a “click”). Most weak-value experiments have been done with photons, but the heralded preparation of a single photon is difficult and slow of rate. Here, we show that the weak value mentioned above can be measured using strong (many-photon) coherent states, while still needing only a click detector such as an avalanche photodiode. One simply subtracts the no-click weak value from the click weak-value and scales the answer by a simple function of the click probability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.001
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 head0.058
GPT teacher head0.304
Teacher spread0.246 · 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