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Record W4381588103 · doi:10.1021/acs.nanolett.3c01228

Gigahertz Detection Rates and Dynamic Photon-Number Resolution with Superconducting Nanowire Arrays

2023· article· en· W4381588103 on OpenAlexfundno aff
Giovanni V. Resta, Lorenzo Stasi, Matthieu Perrenoud, Sylvain El-Khoury, Tiff Brydges, Rob Thew, Hugo Zbinden, Félix Bussières

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsnot available
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsNational Center of Competence in Research Quantum Science and TechnologyNational Research Council CanadaInnosuisse - Schweizerische Agentur für InnovationsförderungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPhotonPhotonicsNanowirePhysicsDetectorPhoton countingDynamic rangeOptoelectronicsHigh dynamic rangeOpticsRealization (probability)QuantumTemporal resolutionQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

Superconducting nanowire single-photon detectors (SNSPDs) have enabled the realization of several quantum optics technologies thanks to their high system detection efficiency (SDE), low dark counts, and fast recovery time. However, the widespread use of linear optical quantum computing, quasi-deterministic single-photon sources, and quantum repeaters requires even faster detectors that can also distinguish between different photon-number states. Here, we present an SNSPD array composed of 14 independent pixels, achieving an SDE of 90% in the telecommunications band. By reading each pixel of the array independently, we show detection of telecommunication photons at 1.5 GHz with 45% absolute SDE. We exploit the dynamic photon-number resolution of the array to demonstrate accurate state reconstruction for a wide range of light inputs, including operation with long-duration light pulses, as obtained with some cavity-based sources. We show two-photon and three-photon fidelities of 74% and 57%, respectively, which represent state-of-the-art results for fiber-coupled SNSPDs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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

Citations49
Published2023
Admission routes1
Has abstractyes

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