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Record W4296695926 · doi:10.1063/5.0114887

Absorptive filters for quantum circuits: Efficient fabrication and cryogenic power handling

2022· article· en· W4296695926 on OpenAlexafffund
Alexandre J. Paquette, J. Griesmar, Gabriel Lavoie, Romain Albert, Florian Blanchet, Alexander Grimm, Ulrich Martel, M. Hofheinz

Bibliographic record

VenueApplied Physics Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsFabricationElectronic circuitOptoelectronicsMaterials scienceQuantumCryogenicsPower (physics)NanotechnologyPhysicsElectrical engineeringEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

We present an efficient fabrication method for absorptive microwave filters based on Eccosorb CR-124. Filters are fabricated from readily available parts, and their cutoff frequency can be set by their length. They exhibit desirable properties such as a very large and deep stop band with rejection beyond 120 dB at least up to 40 GHz, more than 10 dB return loss in both the pass and the stop band, and an error-function shaped step response without overshoot. Measurements at very low temperatures show that the filters thermalize on a time scale of approximately 100 s, and that they can absorb power as high as 100 nW with their noise temperature staying remarkably low, below 100 mK. These properties make the filters ideal for cryogenic filtering and filtering of intermediate frequency port signals of mixers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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 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

Citations8
Published2022
Admission routes2
Has abstractyes

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