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Record W2735443200 · doi:10.1139/cjp-2017-0227

High-fidelity generating multi-qubit W state via dressed states in the system of multiple resonators coupled with a superconducting qubit

2017· article· en· W2735443200 on OpenAlexvenueno aff
Yufeng Yang, Ye‐Hong Chen, Qi‐Cheng Wu, Zhi‐Cheng Shi, Jie Song, Yan Xia

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhysicsQubitQuantum decoherenceQuantum mechanicsAdiabatic processResonatorFlux qubitCharge qubitPhase qubitDissipationState (computer science)Bell stateQuantumFidelityTopology (electrical circuits)AlgorithmOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

In this paper, we present an alternative scheme to generate W state of three superconducting qubits in three spatially separated coplanar waveguide resonators with the quantum Zeno dynamics and the dressed states. When a set of dressed states is suitably chosen, a scheme can be designed for accelerating the adiabatic passage without additional couplings. What is more, the populations of the intermediate states of the system can be restricted by choosing suitable control parameters. We discuss the influence of dissipation and operational imperfection by numerical analysis and the results show that the scheme is robust against various decoherence processes. In addition, we hope the scheme can provide a theoretical basis for the manipulation of the multi-qubit quantum state.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.231
Teacher spread0.207 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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