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Record W4401240307 · doi:10.1117/12.3025759

Efficient broadband quantum memory using ultracold atoms

2024· article· en· W4401240307 on OpenAlexaff
Lindsay J. LeBlanc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBroadbandQuantum memoryQuantumPhysicsUltracold atomComputer scienceAtom opticsOptoelectronicsQuantum computerQuantum networkAtomic physicsQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

Neutral atomic gases provide fantastic opportunities for studying and controlling quantum phenomena, ranging from many-body physics to quantum computers. In our research, we use the well-known interactions between cold gases and electromagnetic radiation to harness various quantum degrees of freedom. Quantum memories, used for storing and manipulating photonic signals, will be a key component in quantum communications systems, especially in realizing critical quantum repeater infrastructure. Cold atoms have significant potential as high performance spin-wave quantum memories, due to the long storage times associated with low temperature and slow thermal diffusion. In our work, we demonstrate two memory protocols in ultracold (sometimes Bose-condensed) atoms, which hold the potential for high-performance light storage: the Autler-Townes splitting (ATS) and superradiant approaches. These methods provide a path towards practical implementations in both ground- and satellite-based quantum communications systems, and we are working on both increasing performance and developing practical implementations.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.259
Teacher spread0.245 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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