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Record W4415513696 · doi:10.1103/22rx-v855

Spectral diffusion of nanomechanical resonators due to single quantum defects

2025· article· en· W4415513696 on OpenAlexfundno aff
M. P. Maksymowych, Mert Yüksel, Oliver A. Hitchcock, Nathan Lee, Felix M. Mayor, Wentao Jiang, M. L. Roukes, Amir H. Safavi‐Naeini

Bibliographic record

VenuePhysical Review Applied · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Science FoundationAmazon Web ServicesGordon and Betty Moore FoundationU.S. Department of Energy
KeywordsResonatorQuantum decoherenceCoupling (piping)Noise (video)Excited stateDissipationSpectral densityQuantum

Abstract

fetched live from OpenAlex

Nanomechanical resonators promise diverse applications from mass spectrometry to quantum information processing, requiring long phonon lifetimes and frequency stability. Although two-level-system (TLS) defects govern dissipation at millikelvin temperatures, the nature of frequency fluctuations remains poorly understood. In nanoscale devices, where acoustic fields are confined to subwavelength volumes, strong coupling to individual TLSs should dominate over defect ensemble effects. In this work, we monitor fast spectral diffusion of phononic crystal nanomechanical resonators while varying the temperature (10 mK--1 K), drive power (${10}^{2}$--${10}^{5}$ phonons), and phononic band structure. We consistently observe random telegraph signals (RTSs), which we attribute to state transitions of individual TLSs. The spectral diffusion is well explained by mechanical coupling to individual far-off-resonant TLSs, which are either thermally excited or strongly coupled to thermal fluctuators. Understanding this fundamental decoherence process, particularly its RTS structure, opens a clear path toward noise suppression for quantum and sensing applications.

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 categoriesnone
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 score0.130
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2025
Admission routes1
Has abstractyes

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