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Record W2101282893 · doi:10.1063/1.4754634

Insertable system for fast turnaround time microwave experiments in a dilution refrigerator

2012· article· en· W2101282893 on OpenAlexaff
Florian Ong, Jean-Luc Orgiazzi, A. de Waard, G. Frossati, Adrian Lupaşcu

Bibliographic record

VenueReview of Scientific Instruments · 2012
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDilution refrigeratorCryostatMicrowaveTurnaround timeBandwidth (computing)Refrigerator carRefrigerationMaterials scienceNuclear engineeringOptoelectronicsOpticsElectrical engineeringElectronic engineeringComputer sciencePhysicsEngineeringSuperconductivityMechanical engineeringTelecommunicationsCondensed matter physics

Abstract

fetched live from OpenAlex

Microwave experiments in dilution refrigerators are a central tool in the field of superconducting quantum circuits and other research areas. This type of experiments relied so far on attaching a device to the mixing chamber of a dilution refrigerator. The minimum turnaround time in this case is a few days as required by cooling down and warming up the entire refrigerator. We developed a new approach, in which a suitable sample holder is attached to a cold-insertable probe and brought in contact with transmission lines permanently mounted inside the cryostat. The total turnaround time is 8 h if the target temperature is 80 mK. The lowest attainable temperature is 30 mK. Our system can accommodate up to six transmission lines, with a measurement bandwidth tested from zero frequency to 12 GHz. This bandwidth is limited by low-pass components in the setup; we expect the intrinsic bandwidth to be at least 18 GHz. We present our setup, discuss the experimental procedure, and give examples of experiments enabled by this system. This new measurement method will have a major impact on systematic ultra-low temperature studies using microwave signals, including those requiring quantum coherence.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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