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Tunable coupler for mediating interactions between a two-level system and a waveguide from a decoupled state to the ultrastrong coupling regime

2023· article· en· W4386443784 on OpenAlexafffund
N. Janzen, Xi Dai, S. Ren, Jiahao Shi, Adrian Lupaşcu

Bibliographic record

VenuePhysical Review Research · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooIndustry CanadaCanada First Research Excellence FundInnovation, Science and Economic Development CanadaOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsPhysicsCoupling (piping)WaveguideMicrowaveCoupling strengthQubitPhotonicsState (computer science)Quantum mechanicsSpin (aerodynamics)QuantumBosonCondensed matter physicsComputer science

Abstract

fetched live from OpenAlex

Two-level systems (TLSs) coupled to waveguides are a fundamental paradigm for light-matter interactions and quantum networks. We introduce and experimentally demonstrate a method to tune the interaction between a TLS, implemented as a flux qubit, and a transmission line waveguide from a decoupled state to a coupling strength that is a significant fraction of the TLS transition frequency, near the ultrastrong coupling regime. The coupling, controlled via magnetic flux, is described by a normalized coupling strength $\ensuremath{\alpha}$ that is measured to range between $6.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$ and $2.19\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}2}$, with larger attainable maximum values predicted by a circuit model of the device. This system enables future investigations in the dynamics of the spin-boson model, microwave photonics, and relativistic quantum information.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.975
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.154
GPT teacher head0.440
Teacher spread0.286 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

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