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Correlation measurements of individual microwave photons emitted from a symmetric cavity

2011· article· en· W1963489215 on OpenAlexaff
Deniz Bozyigit, C. B. Lang, L. Steffen, J. M. Fink, Christopher Eichler, Matthias Baur, R. Bianchetti, Peter Leek, Stefan Filipp, Andreas Wallraff, Marcus P. da Silva, Alexandre Blais

Bibliographic record

VenueJournal of Physics Conference Series · 2011
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversité de Sherbrooke
FundersBranco Weiss Fellowship – Society in ScienceAlfred P. Sloan Foundation
KeywordsPhotonMicrowavePhysicsMicrowave cavityOpticsAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Superconducting circuits have been successfully established as systems to prepare and investigate microwave light fields at the quantum level. In contrast to optical experiments where light is detected using photon counters, microwaves are usually measured with well developed linear amplifiers. This makes measurements of correlation functions - one of the important tools in optics - harder to achieve because they traditionally rely on photon counters and beam splitters. Here, we demonstrate a system where we can prepare on demand single microwave photons in a cavity and detect them at the two outputs of the cavity using linear amplifiers. Together with efficient data processing, this allows us to measure different observables of the cavity photons, including the first-order correlation function. Using these techniques we demonstrate cooling of a thermal background field in the cavity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.239
Teacher spread0.166 · 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 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

Citations5
Published2011
Admission routes1
Has abstractyes

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