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Record W4378804394 · doi:10.1117/12.2654453

Scalable architectures for CMOS and telecom-compatible quantum information processing

2023· preprint· en· W4378804394 on OpenAlexaff
Nicola Montaut, Piotr Roztocki, Hao Yu, Mario Chemnitz, Stefania Sciara, Y. Jestin, Benjamin MacLellan, Bennet Fischer, Michael Kues, Christian Reimer, Luis R. Cortes, Benjamin Wetzel, Yanbing Zhang, Sébastien Loranger, Raman Kashyap, Alfonso Carmelo Cino, Brent E. Little, David J. Moss, Lucia Caspani, William J. Munro, José Azaña, Roberto Morandotti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceQuantum entanglementQuantum computerScalabilityPhotonicsQuantum technologyQuantum networkQuantum informationQuantum sensorQuantumElectronic engineeringComputer architecturePhysicsEngineeringQuantum mechanicsOpen quantum system

Abstract

fetched live from OpenAlex

Quantum technologies harness nonclassical features of particles, here, photons, to develop novel, efficient, and precise devices for information processing applications. Superposition, entanglement, as well as the coherent manipulation of quantum states are at the heart of the second quantum revolution (quantum 2.0) which targets the development of secure cryptographic systems, complex computation protocols, and more. Emerging quantum architectures rely on the realistic implementation of photonic schemes which are scalable, resource-efficient, and compatible with CMOS technologies as well as fiber networks. This work demonstrates current schemes utilized for time-/frequency-bin entanglement generation and processing by leveraging existing telecommunications and integrated photonics infrastructures.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

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 communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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