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Dissipation-Based Quantum Sensing of Magnons with a Superconducting Qubit

2020· article· en· W3026681027 on OpenAlexfundno aff
Samuel Wolski, Dany Lachance-Quirion, Yutaka Tabuchi, S. Kono, Atsushi Noguchi, Koji Usami, Yasunobu Nakamura

Bibliographic record

VenuePhysical Review Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsnot available
FundersExploratory Research for Advanced TechnologyJapan Science and Technology AgencyFonds de recherche du Québec – Nature et technologiesJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology
KeywordsDissipationQubitSuperconductivityPhysicsMagnonCharge qubitPhase qubitQuantumCondensed matter physicsQuantum mechanicsFerromagnetism

Abstract

fetched live from OpenAlex

Hybrid quantum devices expand the tools and techniques available for quantum sensing in various fields. Here, we experimentally demonstrate quantum sensing of a steady-state magnon population in a magnetostatic mode of a ferrimagnetic crystal. Dispersively coupling the magnetostatic mode to a superconducting qubit allows for the detection of magnons using Ramsey interferometry with a sensitivity on the order of 10^{-3} magnons/sqrt[Hz]. The protocol is based on dissipation as dephasing via fluctuations in the magnetostatic mode reduces the qubit coherence proportionally to the number of magnons.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations162
Published2020
Admission routes1
Has abstractyes

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