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Record W3096094854 · doi:10.1088/1367-2630/abf1d9

Storing short single-photon-level optical pulses in Bose–Einstein condensates for high-performance quantum memory

2021· article· en· W3096094854 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Journal of Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRealization (probability)Quantum memoryQuantumProtocol (science)Quantum networkQuantum computerCharacter (mathematics)

Abstract

fetched live from OpenAlex

Abstract Large-scale quantum networks require quantum memories featuring long-lived storage of non-classical light together with efficient, high-speed and reliable operation. The concurrent realization of these features is challenging due to inherent limitations of matter platforms and light–matter interaction protocols. Here, we propose an approach to overcome this obstacle, based on the implementation of the Autler–Townes-splitting (ATS) quantum-memory protocol on Bose–Einstein condensate (BEC) platform. We demonstrate a proof-of-principle of this approach by storing short pulses of single-photon-level light as a collective spin-excitation in a rubidium-BEC. For 20 ns long-pulses, we achieve an ultra-low-noise memory with an efficiency of 30% and lifetime of 15 μ s. The non-adiabatic character of the ATS protocol (leading to high-speed and low-noise operation) in combination with the intrinsically large atomic densities and ultra-low temperatures of the BEC platform (offering highly efficient and long-lived storage) opens up a new avenue toward high-performance quantum memories.

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.

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 score0.271
Threshold uncertainty score0.651

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 head0.043
GPT teacher head0.281
Teacher spread0.237 · 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