Lanthanide‐Based Quantum Optical Materials
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".