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Record W4386876363 · doi:10.48550/arxiv.2309.09956

Small k-pairable states

2023· preprint· en· W4386876363 on OpenAlexaff
Nathan Claudet, Mehdi Mhalla, Simon Perdrix

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsFuture Earth
FundersQuantERAAgence Nationale de la Recherche
KeywordsCombinatoricsMathematicsVertex (graph theory)Discrete mathematicsQubitGraphQuantumPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

A $k$-pairable $n$-qubit state is a resource state that allows Local Operations and Classical Communication (LOCC) protocols to generate EPR-pairs among any $k$-disjoint pairs of the $n$ qubits. Bravyi et al. introduced a family of $k$-pairable $n$-qubit states, where $n$ grows exponentially with $k$. Our primary contribution is to establish the existence of 'small' pairable quantum states. Specifically, we present a family of $k$-pairable $n$-qubit graph states, where $n$ is polynomial in $k$, namely $n=O(k^3\ln^3k)$. Our construction relies on probabilistic methods. Furthermore, we provide an upper bound on the pairability of any arbitrary quantum state based on the support of any local unitary transformation that has the shared state as a fixed point. This lower bound implies that the pairability of a graph state is at most half of the minimum degree up to local complementation of the underlying graph, i.e., $k(|G \rangle)\le \lceil δ_{loc}(G)/2\rceil$. We also investigate the related combinatorial problem of $k$-vertex-minor-universality: a graph $G$ is $k$-vertex-minor-universal if any graph on any $k$ of its vertices is a vertex-minor of $G$. When a graph is $2k$-vertex-minor-universal, the corresponding graph state is $k$-pairable. More precisely, one can create not only EPR-pairs but also any stabilizer state on any $2k$ qubits through local operations and classical communication. We establish the existence of $k$-vertex-minor-universal graphs of order $O(k^4 \ln k)$. Finally, we explore a natural extension of pairability in the presence of errors or malicious parties and show that vertex-minor-universality ensures a robust form of pairability.

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)0.005
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.083
GPT teacher head0.182
Teacher spread0.098 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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