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Record W4416227809 · doi:10.1002/adfm.202524562

Lanthanide‐Based Quantum Optical Materials

2025· article· en· W4416227809 on OpenAlexaff
Kai Huang, Joshua Fung‐A‐Fat, Jiaze Wu, Shupei Yu, Daniel Fung‐A‐Fat, Kangyao Deng, Gwenyth Zuercher, Ishana Saroha, Weichu Xu, Gang Han

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum sensorQuantum technologyQuantumCoherence (philosophical gambling strategy)Quantum information scienceQuantum networkQuantum imagingQuantum opticsQuantum informationMacroscopic quantum phenomena

Abstract

fetched live from OpenAlex

Abstract Quantum optical materials are fundamental infrastructure to realize emerging quantum technologies for quantum communication, quantum computation, quantum memory, and sensing. Among the wide range of solid‐state platforms explored, lanthanide‐based systems stand out for their ability to combine atomic‐like coherence with the scalability of condensed matter. The shielded 4f orbitals of lanthanide ions yield long‐lived radiative transitions, narrow homogeneous linewidths, and rich hyperfine structures that support optical and spin coherence extending from milliseconds to hours. These intrinsic properties, combined with the versatility of host environments—from bulk crystals and thin films to fibers and nanocrystals—enable lanthanides to address three pillars of quantum optics: collective emission, single‐ion emission, and ensemble‐based quantum memories. Recent experiments have demonstrated room‐temperature superfluorescence in nanocrystals, single‐ion emission in the telecom band, and quantum memories with record‐setting storage times and efficiencies. Here, a critical review of these advances, emphasizing how control parameters such as host lattice, isotopic purification, dopant concentration, and photonic integration govern performance metrics of lanthanide‐based quantum optical materials, is provided. By analyzing the state‐of‐the‐art of lanthanides for quantum optics and envisioning the potential future directions, the principles to design lanthanide‐based materials as indispensable building blocks for scalable, application‐driven quantum technologies are aimed to highlight.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.254
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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