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Record W4404581708 · doi:10.1088/1367-2630/ad95b3

Generation of non-Gaussian states of light using deterministic photon subtraction

2024· article· en· W4404581708 on OpenAlexaff
Abdolreza Pasharavesh, Michal Bajcsy

Bibliographic record

VenueNew Journal of Physics · 2024
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhysicsPhotonQuantum mechanicsGaussianNonclassical lightCoherent statesQuantum

Abstract

fetched live from OpenAlex

Abstract We explore a recently demonstrated deterministic photon subtraction scheme, based on single-photon Raman interaction with a Λ-type three-level atom, as a tool for manipulating quantum state of few-photon light pulses. We establish a comprehensive theoretical framework using input–output formalism and quantum regression theorem, enabling calculation of the first order autocorrelation matrices of the output light and identification of the temporal modes present in the generated light via their eigendecomposition. By modeling the entire system as a quantum network consisting multiple virtual cavities and a lambda-type emitter cascaded in two parallel guided modes of opposite propagation directions, we extract the quantum state occupying the modes of interest. For both squeezed vacuum and coherent light input pulses, the Wigner function of the output light after photon subtraction clearly reveals its non-Gaussian character. Furthermore, we propose a measurement-based scheme on the subtracted photon which can lead to conditional generation of quantum states resembling Schrodinger’s kitten state directly from coherent input light with fidelities above 99%. This result is particularly nothworthy, as coherent pulses, unlike the squeezed vacuum inputs commonly used in previous studies, are readily available in most experimental setups.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.214

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 communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.292
Teacher spread0.255 · 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 teacher head, 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

Citations9
Published2024
Admission routes1
Has abstractyes

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