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Record W2253267851 · doi:10.1002/lpor.201500133

Deterministic separation of arbitrary photon pair states in integrated quantum circuits

2016· article· en· W2253267851 on OpenAlexaff

Bibliographic record

VenueLaser & Photonics Review · 2016
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum entanglementPhotonSeparation (statistics)Interference (communication)QuantumReduction (mathematics)Dispersion (optics)Quantum opticsMultiplexingWork (physics)

Abstract

fetched live from OpenAlex

Abstract Entangled photon pairs must often be spatially separated for their subsequent manipulation in integrated quantum circuits. Separation that is both deterministic and universal can in principle be achieved through anti‐coalescent two‐photon quantum interference. However, such interference‐facilitated pair separation (IFPS) has not been extensively studied in the integrated setting, which has important implications on performance. This work provides a detailed review of IFPS and examines how integrated device dependencies such as dispersion impact separation fidelity and interference visibility. The analysis applies equally to both on‐chip and in‐fiber implementations. When coupler dispersion is present, the separation performance can depend on photon bandwidth, spectral entanglement and the dispersion. By design, reduction in the separation fidelity due to loss of non‐classical interference can be perfectly compensated for by classical wavelength demultiplexing effects. This work informs the design of devices for universal photon pair separation of states with tunable arbitrary properties. image

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.277
Teacher spread0.260 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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