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Record W4386737887 · doi:10.48550/arxiv.2309.05766

Efficient two-qutrit gates in superconducting circuits using parametric coupling

2023· preprint· en· W4386737887 on OpenAlexafffund
Mahadevan Subramanian, Adrian Lupaşcu

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsQutritTransmonTopology (electrical circuits)Electronic circuitComputer scienceCoupling (piping)Logic gateProtocol (science)Parametric statisticsScalabilityPhysicsElectronic engineeringQuantum mechanicsMathematicsMaterials scienceElectrical engineeringEngineeringAlgorithmQubit

Abstract

fetched live from OpenAlex

Recently, significant progress has been made in the demonstration of single qutrit and coupled qutrit gates with superconducting circuits. Coupled qutrit gates have significantly lower fidelity than single qutrit gates, owing to long implementation times. We present a protocol to implement the CZ universal gate for two qutrits based on a decomposition involving two partial state swaps and local operations. The partial state swaps can be implemented effectively using parametric coupling, which is fast and has the advantage of frequency selectivity. We perform a detailed analysis of this protocol in a system consisting of two fixed-frequency transmons coupled by a flux-tunable transmon. The application of an AC flux in the tunable transmon controls the parametric gates. This protocol has the potential to lead to fast and scalable two-qutrit gates in superconducting circuit architectures.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.172
GPT teacher head0.229
Teacher spread0.057 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes2
Has abstractyes

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