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Dynamics of Transmon Ionization

2022· article· en· W4226401025 on OpenAlexafffund
Ross Shillito, Alexandru Petrescu, Joachim Cohen, Jackson Beall, Markus Hauru, Martin Ganahl, Adam G. M. Lewis, Guifré Vidal, Alexandre Blais

Bibliographic record

VenuePhysical Review Applied · 2022
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsCanadian Institute for Advanced ResearchUniversité de Sherbrooke
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaOffice of ScienceCanada First Research Excellence FundOntario Ministry of Research, Innovation and ScienceGovernment of CanadaInstitut de Ciències FotòniquesInstitut Périmètre de physique théoriqueInnovation, Science and Economic Development CanadaU.S. Department of Energy
KeywordsTransmonQubitPhysicsSpurious relationshipFidelitySemiclassical physicsQuantumQuantum computerCircuit quantum electrodynamicsResonatorPhase qubitIonizationQuantum mechanicsComputer scienceOptoelectronicsIonTelecommunications

Abstract

fetched live from OpenAlex

Qubit measurement is an essential step in any quantum computation. In circuit quantum electrodynamics, a leading quantum computer architecture, qubit readout is commonly one of the longest and lowest-fidelity processes. The authors numerically explore the dynamics of a driven transmon-resonator system under strong, nearly resonant measurement drives to better understand this issue. They find clear signs of transmon ``ionization'', in which the qubit escapes its confining potential under the influence of the drive, and semiclassical methods then reveal the mechanism. This approach can be used to optimize circuit parameters, suppress these spurious effects, and increase readout fidelity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations90
Published2022
Admission routes2
Has abstractyes

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