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Record W4378070950 · doi:10.1021/acs.nanolett.3c01551

Scalable Generation and Detection of on-Demand W States in Nanophotonic Circuits

2023· article· en· W4378070950 on OpenAlexaff
Jun Gao, Leonardo Santos, Govind Krishna, Ze-Sheng Xu, Adrian Iovan, Stephan Steinhauer, Otfried Gühne, Philip J. Poole, Dan Dalacu, Val Zwiller, Ali W. Elshaari

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsNational Research Council Canada
FundersH2020 European Research CouncilVetenskapsrådetWallenberg Center for Quantum Technology, Chalmers University of TechnologyWallenberg Centre for Molecular and Translational MedicineChinesisch-Deutsche Zentrum für WissenschaftsförderungGöran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk ForskningGöran Gustafssons StiftelserUniversität SiegenBundesministerium für Bildung und ForschungVINNOVAKnut och Alice Wallenbergs StiftelseDeutsche Forschungsgemeinschaft
KeywordsQuantum entanglementMultipartite entanglementPhotonicsQuantum sensorOne-way quantum computerQuantum imagingQuantum teleportationQuantum technologyComputer scienceW statePhotonQuantum computerQuantum informationPhoton entanglementQuantum networkQuantum statePhysicsQuantum mechanicsQuantumSquashed entanglementOpen quantum system

Abstract

fetched live from OpenAlex

Quantum physics phenomena, entanglement and coherence, are crucial for quantum information protocols, but understanding these in systems with more than two parts is challenging due to increasing complexity. The W state, a multipartite entangled state, is notable for its robustness and benefits in quantum communication. Here, we generate eight-mode on-demand single-photon W states, using nanowire quantum dots and a silicon nitride photonic chip. We demonstrate a reliable and scalable technique for reconstructing the W state in photonic circuits using Fourier and real-space imaging, supported by the Gerchberg-Saxton phase retrieval algorithm. Additionally, we utilize an entanglement witness to distinguish between mixed and entangled states, thereby affirming the entangled nature of our generated state. The study provides a new imaging approach of assessing multipartite entanglement in W states, paving the way for further progress in image processing and Fourier-space analysis techniques for complex quantum systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.213

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)0.0000.000

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 head0.216
Teacher spread0.203 · 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 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

Citations20
Published2023
Admission routes1
Has abstractyes

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