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Record W4388092905 · doi:10.48550/arxiv.2310.18838

High Q-factor diamond optomechanical resonators with silicon vacancy centers at millikelvin temperatures

2023· preprint· en· W4388092905 on OpenAlexfundno aff
Graham Joe, Cleaven Chia, Benjamin Pingault, Michael Haas, Michelle Chalupnik, Eliza Cornell, Kazuhiro Kuruma, Bartholomeus Machielse, Neil Sinclair, Srujan Meesala, Marko Lončar

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsNatural Sciences and Engineering Research Council of CanadaArmy Research OfficeAir Force Office of Scientific ResearchAgency for Science, Technology and ResearchDivision of Materials ResearchJapan Society for the Promotion of ScienceInstitute for Quantum Information and Matter, California Institute of TechnologyEuropean CommissionOffice of Naval ResearchHarvard UniversityNational Science Foundation
KeywordsDiamondPhononLaser linewidthResonatorSiliconVacancy defectMaterials scienceOptoelectronicsSpin (aerodynamics)Condensed matter physicsPhysicsOptics

Abstract

fetched live from OpenAlex

Phonons are envisioned as coherent intermediaries between different types of quantum systems. Engineered nanoscale devices such as optomechanical crystals (OMCs) provide a platform to utilize phonons as quantum information carriers. Here we demonstrate OMCs in diamond designed for strong interactions between phonons and a silicon vacancy (SiV) spin. Using optical measurements at millikelvin temperatures, we measure a linewidth of 13 kHz (Q-factor of ~440,000) for 6 GHz acoustic modes, a record for diamond in the GHz frequency range and within an order of magnitude of state-of-the-art linewidths for OMCs in silicon. We investigate SiV optical and spin properties in these devices and outline a path towards a coherent spin-phonon interface.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.191
Teacher spread0.154 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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