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Programmable Silicon Photonic Sources of Frequency Bin Entangled Qubits and Qudits

2023· article· en· W4385655953 on OpenAlexaff
Massimo Borghi, Noemi Tagliavacche, Marco Clementi, Federico Andrea Sabattoli, Linda Gianini, Houssein El Dirani, Laurène Youssef, Nicola Bergamasco, Camille Petit-Étienne, E. Pargon, J. E. Sipe, Marco Liscidini, Corrado Sciancalepore, Mattéo Galli, Daniele Bajoni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsQubitPhotonicsQuantum computerPhotonResonatorComputer sciencePhysicsQuantum opticsElectronic engineeringOptoelectronicsQuantumOpticsQuantum mechanicsEngineering

Abstract

fetched live from OpenAlex

Frequency bin encoding is becoming of widespread use in photonic quantum applications due to its strong noise resilience, the large information content that can be encoded per photon, and the inherent parallelism through which gates can be applied over multiple frequency modes. Ideally, sources of frequency bin qubits and qudits should be programmable, have high brightness, and have tightly spaced spectral modes to be useful for quantum algorithms. Here we present an integrated photonic device that simultaneously met all these requirement. We exploit the coherent generation of photon pairs over multiple ring resonators to overcome the trade-offs that hurdle single resonators. Harnessing the dense integration and phase stability of silicon photonics, we demonstrate a photon pair source of frequency bin qudits and qudits with simultaneously high brightness, fidelity, and purity.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.243
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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