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Record W4402634024 · doi:10.1139/cjp-2023-0190

QEYSSat 2.0—white paper on satellite-based quantum communication missions in Canada

2024· article· en· W4402634024 on OpenAlexaffvenueabout
Thomas Jennewein, Christoph Simon, André Fougères, François Babin, Faezeh Kimiaee Asadi, Katanya B. Kuntz, Mathieu Maisonneuve, Brian Moffat, Kimia Mohammadi, Denis Panneton

Bibliographic record

VenueCanadian Journal of Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversity of CalgaryInstitut National d'OptiqueUniversity of Waterloo
Fundersnot available
KeywordsPhysicsSatelliteQuantum information scienceCommunications satelliteRemote sensingQuantumAstrobiologyAstronomyQuantum mechanicsQuantum entanglementGeography

Abstract

fetched live from OpenAlex

We present the white paper developed during the QEYSSat 2.0 study, which was undertaken between June 2021 and March 2022. The study objective was to establish a technology road-map for a Canada-wide quantum network enabled by satellites. We survey the state-of-art in quantum communication technologies, identify the main applications and architectures, review the technical readiness levels and technology bottlenecks and identify a future mission scenario. We report the findings of a dedicated one-day workshop that included Canadian stakeholders from government, industry and academia to gather inputs and insights for the applications and technical road-map. We also provide an overview of the Quantum EncrYption and Science Satellite (QEYSSat) mission expected to launch in 2024–2025 and its anticipated outcomes. One of the main outcomes of this study is that developing the main elements for a Canada-wide quantum internet will have the highest level of impact, which includes Canada-wide entanglement distribution and teleportation. We present and analyze a possible future mission (“QEYSSat 2.0”) that would enable long-range quantum teleportation across Canada as an important step towards this vision.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.221
Teacher spread0.197 · 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 designNot applicable
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

Citations11
Published2024
Admission routes3
Has abstractyes

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