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Record W4221009734 · doi:10.1117/12.2606059

A perspective on silicon photonic quantum computing with spin qubits

2022· article· en· W4221009734 on OpenAlexaff
Andreas Pfenning, Xiruo Yan, Sebastian Gitt, Joshua Fabian, Becky Lin, Donald Witt, Abdelrahman E. Afifi, Adan Azem, Adam Darcie, Jingda Wu, Matthew Mitchell, Kashif M. Awan, Lukas Chrostowski, Jeff F. Young

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQubitQuantum computerQuantum entanglementPhotonicsCoherence (philosophical gambling strategy)Cluster stateQuantum sensorComputer sciencePhotonPhysicsInitializationQuantum opticsQuantum technologyQuantum networkElectronic engineeringQuantumQuantum mechanicsOpen quantum systemEngineering

Abstract

fetched live from OpenAlex

We recently proposed a quantum computing platform that exploits circuit-bound photons to create cluster states and achieve one-way measurement-based quantum computations on arrays of photonically interfaced solid-state spin qubits with long coherence-times. Single photons are used for spin initialization, readout and for photon-mediated long-range entanglement creation. In this conference talk, we elaborate on the challenges that are faced during any practical implementation of this architecture by breaking it down into the key physical building blocks. We further discuss the constraints imposed on the spin qubits and the photonic circuit components that are set by the requirements of achieving fault-tolerant performance.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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