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Record W4412622203 · doi:10.1103/v5k1-whwz

Photon-Interfaced Ten-Qubit Register of Trapped Ions

2025· article· en· W4412622203 on OpenAlexfundno aff
Zhe Xian Koong, J. Bate, A. Winkler, Viktor Krutianskii, B. P. Lanyon

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsnot available
FundersNextGenerationEUAustrian Science FundEuropean CommissionÖsterreichische Nationalstiftung für Forschung, Technologie und EntwicklungHorizon 2020 Framework ProgrammeCanadian Institute for Advanced Research
KeywordsIonQubitPhysicsPhotonRegister (sociolinguistics)Atomic physicsNuclear physicsQuantumOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Establishing networks of quantum processors offers a path to scalable quantum computing and applications in communication and sensing. This requires first developing efficient interfaces between photons and multiqubit registers. In this Letter, we show how to entangle each individual matter qubit in a register of ten to a separate traveling photon. The qubits are encoded in a string of cotrapped atomic ions. By switching the trap confinement, ions are brought one at a time into the waist of an optical cavity and emit a photon via a laser-driven cavity-mediated Raman transition. The result is a train of photonic qubits, each near-maximally entangled by their polarization with a different ion qubit in the string. An average ion-photon Bell state fidelity of 92% is achieved, for an average probability for detecting each single photon of 9%. The technique is directly scalable to larger ion-qubit registers and opens up the near-term possibility of entangling distributed networks of trapped-ion quantum processors, sensing arrays, and clocks.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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