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Record W3139827858

Quantum Distributed Consensus

2007· article· en· W3139827858 on OpenAlexaboutno aff
Louie Helm, Christine Julien

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsnot available
Fundersnot available
KeywordsImpossibilityComputer scienceConsensusTheoretical computer scienceAsynchronous communicationNotationDistributed algorithmQuantum computerDistributed computingUniform consensusByzantine fault toleranceQuantumFault toleranceMathematicsMulti-agent systemQuantum mechanicsArtificial intelligencePhysics
DOInot available

Abstract

fetched live from OpenAlex

Distributed consensus is possible for asynchronous communication networks when assisted by quantum mechanical effects. Previous results are extended to show that consensus is possible even in the presence of byzantine process failures. This result directly contradicts the FLP impossibility result which states that distributed consensus is impossible when even one faulty networked process exists in the group. PODC’08, August 18–21, 2008, p445, Toronto, Ontario, Canada. ACM 978-1-59593-9890/08/08. 1. DISTRIBUTED COMPUTING IS PHYSICAL Distributed computing is concerned with what can be accomplished using systems of networked processors. As with all information processing devices, these systems are necessarily physical and ultimately bound by the laws of physics[Land88]. Therefore, when proving impossibility results and bounds on algorithmic efficiency, it only makes sense to consider the possibilities offered, not only by classical information processing, but also the effects of quantum mechanics[NC00]. Recent results have shown that shared quantum resources make problems like anonymous leader election and distributed consensus possible under conditions where no classical algorithm could perform the same tasks[DP06]. These results are extended here to show that shared quantum resources allow fault-tolerant consensus in direct contradiction of previous impossibility results[FLP85]. Although this research is largely self-contained, a basic understanding of distributed computing is assumed of the reader along with a passing understanding of linear algebra which is necessary to understand the reasoning behind the algorithm detailed in section 4. A thorough understanding of quantum mechanics is recommended, although a reference of the specific quantum mechanical notation required to follow later reasoning in section 4 is detailed in section 3. A computer scientist unfamiliar with quantum mechanics would be well served to reference chapter 2 of [NC00] in order to gain a deeper understanding of the notation covered in section 3 of this paper. Likewise, a quantum physicist would be well served to reference chapter 15 of [Garg04] to more deeply understand the fault modeling of distributed computing covered in section 2 and section 5.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.006
Open science0.0020.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0160.003

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 head0.244
Teacher spread0.233 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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