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Record W3008459412 · doi:10.1117/12.2552541

Variable strength measurements of non-local observables

2020· article· en· W3008459412 on OpenAlexaff
Noah Lupu-Gladstein, Hugo Ferretti, Weng-Kian Tham, Arthur O. T. Pang, Aephraim M. Steinberg, Kent Bonsma-Fisher, Aharon Brodutch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsNational Research Council CanadaCanadian Institute for Advanced ResearchUniversity of Toronto
Fundersnot available
KeywordsObservablePhotonPhysicsQuantumDegenerate energy levelsRealization (probability)DetectorQuantum imagingHidden variable theoryQuantum opticsQuantum mechanicsQuantum technologyOpticsOpen quantum systemMathematicsStatistics

Abstract

fetched live from OpenAlex

Quantum non-demolition measurements play an important role in quantum theory and many of its applications. In theory they are the most fundamental quantum measurements, but in practice their realization can be chal- lenging due to realistic constraints. In optics for example, most measurements are destructive since photons get absorbed by the detector. While some simple single particle non-demolition measurements are routinely done in optical setups by using a second degree of freedom to encode the results at an intermediate stage, measurements of degenerate non-local observables involving multiple photons remain challenging, especially when these are done at intermediate measurement strengths. Here we present an optical setup for performing variable strength non-demolition measurements of non-local observables in a pre and postselected setting. At the heart of the setup is an apparatus that can be used to turn a strong (projective) measurement into an arbitrary strength measurement by using a quantum eraser. We present our initial calibration results for this apparatus.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.235
Teacher spread0.190 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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