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Record W4403421084 · doi:10.23919/emsci.2024.0021

On-Demand Photon Storage and Retrieval with a Solid-State Photon Molecule at Room Temperature

2024· article· en· W4403421084 on OpenAlexaff
Ze-Yu Luo, Tong Zhang, Y. Ye, Yunfei Wang, Chengcheng Yu, Zhiyong Luo, Yijie Zhang, Mo-Chi Xu, Barry C. Sanders, Hui Wang, Chao‐Yang Lu, Jian-Wei Pan

Bibliographic record

VenueElectromagnetic Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsSolid-statePhotonTwo-photon excitation microscopyMoleculeState (computer science)Materials sciencePhysicsOptoelectronicsAtomic physicsComputer scienceOpticsEngineering physicsQuantum mechanicsFluorescenceAlgorithm

Abstract

fetched live from OpenAlex

Deterministically achieving on-chip photon storage and retrieval is a fundamental challenge for integrated photonics. Moreover, this requirement is increasingly urgent as photon storage and retrieval is crucial to realize truly scalable room-temperature quantum computing. However, most of existing quantum memories integrated on chips must either work at cryogenic temperature or else are strongly coupled with the environment, which hugely reduces the efficiency. Here, we propose an on-chip room-temperature quantum memory comprising three coupled microcavities, which presents an ideal dark state decoupled by a waveguide, thereby allowing on-demand photon storage and retrieval with high efficiency and high fidelity simultaneously. Furthermore, we demonstrate that the single-photon temporal duration can be increased or decreased by a factor of 103, thereby enabling many crucial quantum applications. Our error-robust approach highlights the potential for a solid-state photonic molecule for use as on-chip quantum memory and for optical quantum computing.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.218
Teacher spread0.215 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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