Photonic Integrated Quantum Memory in Rare‐Earth Doped Solids
Bibliographic record
Abstract
Abstract An optical quantum memory is a device that can store photonic quantum information and release it after a controlled time. It is an essential component for overcoming channel losses in large‐scale quantum networks. Optical quantum memories have been demonstrated with various physical systems including atomic gases, single atoms in optical cavities, and rare‐earth‐ion doped solids. Now, quantum memories are marching toward miniaturization and integration for large‐scale practical applications. Solid state systems stand as a natural choice due to the physical stability and ease of micro or nano fabrication using well‐established techniques. In the past decade, considerable efforts have been devoted to developing photonic integrated quantum memories, that is, quantum memories based on micro/nano‐photonic structures manufactured in solids. Remarkable performances have been achieved with integrated quantum memories, with the advantages of lower laser/electric power requirements, small volumes, large storage densities, and easy implementations. In this article, the basic concepts of optical quantum memories, the state‐of‐the‐art technologies for fabricating integrated quantum memories in rare‐earth ions doped crystals, and recent advances are introduced, and the roadmap for developing practically useful devices for applications in quantum networks is discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".