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Removing Phase Ambiguity in Fiber-Based Interferometers for Coherent Time-Bin Operations

2019· article· en· W2981185821 on OpenAlexaff
Piotr Roztocki, Mehedi Islam, Christian Reimer, Bennet Fischer, Stefania Sciara, Yanbing Zhang, David Moss, Michael Kues, Roberto Morandotti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAstronomical interferometerBinAmbiguityComputer sciencePhase (matter)InterferometryOpticsPhysicsAlgorithm

Abstract

fetched live from OpenAlex

Time is a practical and robust degree of freedom for the encoding of quantum information. Qubits encoded in so-called `time-bins', allowing a discrete superposition of two potential arrival times, have their entanglement preserved even over long propagation distances in standard fiber networks [1]. Time has also been used for the preparation of more complex quantum systems, such as hyper-entangled and cluster states [2]. These qualities put time-bin encoding at the center of applications ranging from quantum state preparation through to quantum communications and information processing. One of the hallmarks of the scheme is that a nonlinear element has to be pumped with phase-coherent double-pulses, which can be achieved by using unbalanced interferometers [1], making their phase stability the key determinant of both state generation and analysis accuracy. However, the phase ambiguity arising from the periodicity of feedback signals [3], as well as the difficulty of stabilizing on interference maxima/minima, leave progress to be made in fiberbased interferometry for time-bin encodings.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.291
Teacher spread0.279 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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