Quantum edge correspondences and quantum Cuntz–Krieger algebras
Bibliographic record
Abstract
Given a quantum graph G=(B,ψ,A)${\mathcal {G}}=(B,\psi ,A)$, we define a C*-correspondence EG$E_{\mathcal {G}}$ over the non-commutative vertex C*-algebra B$B$, called the quantum edge correspondence. For a classical graph G${\mathcal {G}}$, EG$E_{\mathcal {G}}$ is the usual graph correspondence spanned by the edges of G${\mathcal {G}}$. When the quantum adjacency matrix A:B→B$A\colon B\rightarrow B$ is completely positive, we show that EG$E_{\mathcal {G}}$ is faithful if and only if ker(A)$\ker (A)$ does not contain a central summand of B$B$. In this case, we show that the Cuntz–Pimsner algebra OEG${\mathcal {O}}_{E_{\mathcal {G}}}$ is isomorphic to a quotient of the quantum Cuntz–Krieger algebra O(G)${\mathcal {O}}({\mathcal {G}})$ defined in Brannan, Eifler, Voigt, and Weber (Trans. Am. Math. Soc. Ser. B 9 (2022), 782–826). Moreover, the kernel of the quotient map is shown to be generated by “localized” versions of the quantum Cuntz–Krieger relations, and OEG${\mathcal {O}}_{E_{\mathcal {G}}}$ is shown to be the universal object associated to these local relations. We study in detail some concrete examples and make connections with the theory of Exel crossed products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".