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Record W4390885481 · doi:10.1364/oe.517001

Long-distance transmission of arbitrary quantum states between spatially separated microwave cavities

2024· article· en· W4390885481 on OpenAlexaff
Yu Wang, Qi-Ping Su, Tong Liu, Guo-Qiang Zhang, Wei Feng, Yang Yu, Chui‐Ping Yang

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsPhysicsMicrowaveQuantum networkMicrowave cavityOpticsQuantum information scienceQuantum informationQuantum imagingQuantum opticsQuantum sensorQubitQuantumQuantum entanglementQuantum mechanics

Abstract

fetched live from OpenAlex

Long-distance transmission between spatially separated microwave cavities is a crucial area of quantum information science and technology. In this work, we present a method for achieving long-distance transmission of arbitrary quantum states between two microwave cavities, by using a hybrid system that comprises two microwave cavities, two nitrogen-vacancy center ensembles (NV ensembles), two optical cavities, and an optical fiber. Each NV ensemble serves as a quantum transducer, dispersively coupling with a microwave cavity and an optical cavity, which enables the conversion of quantum states between a microwave cavity and an optical cavity. The optical fiber acts as a connector between the two optical cavities. Numerical simulations demonstrate that our method allows for the transfer of an arbitrary photonic qubit state between two spatially separated microwave cavities with high fidelity. Furthermore, the method exhibits robustness against environmental decay, parameter fluctuations, and additive white Gaussian noise. Our approach offers a promising way for achieving long-distance transmission of quantum states between two spatially separated microwave cavities, which may have practical applications in networked large-scale quantum information processing and quantum communication.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.258
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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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