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Path-entangled radiation from a kinetic inductance amplifier

2024· article· en· W4403640760 on OpenAlexafffund
Abdul Mohamed, Shabir Barzanjeh

Bibliographic record

VenuePhysical Review Applied · 2024
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPhysicsAmplifierPath (computing)Kinetic inductanceQuantum mechanicsInductanceComputer scienceOptoelectronics

Abstract

fetched live from OpenAlex

Continuous-variable entangled radiation known as Einstein-Podolsky-Rosen (EPR) states are spatially separated quantum states with applications ranging from quantum teleportation and communication to quantum sensing. The ability to efficiently generate and harness EPR states is vital for advancements in quantum technologies, particularly in the microwave domain. Here, we introduce a kinetic inductance quantum-limited amplifier that generates stationary-path-entangled microwave radiation. Unlike traditional Josephson junction circuits, our design offers simplified fabrication and operational advantages. By generating single-mode squeezed states and distributing them to different ports of a microwave resonator, we deterministically create distributed entangled states at the output of the resonator. In addition to the experimental verification of entanglement, we present a simple theoretical model using a beam-splitter picture to describe the generation of path-entangled states in kinetic inductance superconducting circuits. This work highlights the potential of kinetic inductance parametric amplifiers as promising technology for practical applications such as quantum teleportation, distributed quantum computing, and enhanced quantum sensing. Moreover, it can contribute to foundational tests of quantum mechanics and advances in next-generation quantum information technologies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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