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Record W1988738349 · doi:10.1002/lpor.200810056

Photon‐echo quantum memory in solid state systems

2009· article· en· W1988738349 on OpenAlexaff
Wolfgang Tittel, Mikael Afzelius, T. Chanelière, R. L. Cone, Stefan Kröll, С. А. Моисеев, Matthew J. Sellars

Bibliographic record

VenueLaser & Photonics Review · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsQuantum information scienceQuantumRepeater (horology)PhysicsQuantum networkPhotonQuantum imagingQuantum stateQuantum sensorQuantum technologyQuantum mechanicsQuantum informationOpen quantum systemComputer scienceQuantum entanglement

Abstract

fetched live from OpenAlex

Abstract Many applications of quantum communication crucially depend on reversible transfer of quantum states between light and matter. Motivated by rapid recent developments in theory and experiment, we review research related to quantum memory based on a photon‐echo approach in solid state material with emphasis on use in a quantum repeater. After introducing quantum communication, the quantum repeater concept, and properties of a quantum memory required to be useful in a quantum repeater, we describe the historical development from spin echoes, discovered in 1950, to photon‐echo quantum memory. We present a simple theoretical description of the ideal protocol, and comment on the impact of a non‐ideal realization on its quantum nature. We extensively discuss rare‐earth‐ion doped crystals and glasses as material candidates, elaborate on traditional photon‐echo experiments as a test‐bed for quantum state storage, and describe the current state‐of‐the‐art of photon‐echo quantum memory. Finally, we give a brief outlook on current research.

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: none
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 communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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

Citations438
Published2009
Admission routes1
Has abstractyes

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