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The deep space quantum link: prospective fundamental physics experiments using long-baseline quantum optics

2022· review· en· W3217688300 on OpenAlexafffund
Makan Mohageg, Luca Mazzarella, Charis Anastopoulos, Jason Gallicchio, B. L. Hu, Thomas Jennewein, Spencer Johnson, Shih-Yuin Lin, Alexander Ling, Christoph Marquardt, Matthias Meister, Raymond Newell, Albert Roura, Wolfgang P. Schleich, Christian Schubert, Dmitry Strekalov, Giuseppe Vallone, Paolo Villoresi, Lisa Wörner, Nan Yu, Aileen Zhai, Paul G. Kwiat

Bibliographic record

VenueEPJ Quantum Technology · 2022
Typereview
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsUniversity of Waterloo
FundersCanadian Space AgencyJet Propulsion LaboratoryMinistry of Science and Technology, TaiwanAgenzia Spaziale ItalianaNASA HeadquartersDipartimenti di EccellenzaDeutsches Zentrum für Luft- und RaumfahrtEuropean CommissionCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsQuantum nonlocalityPhysicsQuantum networkQuantum sensorQuantum information scienceNASA Deep Space NetworkQuantum technologyQuantumQuantum informationQuantum entanglementOpen quantum systemQuantum mechanicsAstronomySpacecraft

Abstract

fetched live from OpenAlex

The National Aeronautics and Space Administration's Deep Space Quantum Link mission concept enables a unique set of science experiments by establishing robust quantum optical links across extremely long baselines. Potential mission configurations include establishing a quantum link between the Lunar Gateway moon-orbiting space station and nodes on or near the Earth. This publication summarizes the principal experimental goals of the Deep Space Quantum Link. These goals, identified through a multi-year design study conducted by the authors, include long-range teleportation, tests of gravitational coupling to quantum states, and advanced tests of quantum nonlocality.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.343
Teacher spread0.295 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations60
Published2022
Admission routes2
Has abstractyes

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