Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".