Qiskit/qiskit-addon-cutting: Circuit Knitting Toolbox 0.8.0
Bibliographic record
Abstract
What's Changed Use BaseSamplerV1 with explicit version (backport #653) by @mergify in https://github.com/Qiskit/qiskit-addon-cutting/pull/654 Pin qiskit-ibm-runtime when running notebooks by @garrison in https://github.com/Qiskit/qiskit-addon-cutting/pull/659 Edit cutting explanation (backport #657) by @mergify in https://github.com/Qiskit/qiskit-addon-cutting/pull/658 Make project's relationship with Qiskit consistent in the copyright headers (backport #646) by @mergify in https://github.com/Qiskit/qiskit-addon-cutting/pull/660 Backport CutQC removal to stable/0.8 branch by @garrison in https://github.com/Qiskit/qiskit-addon-cutting/pull/662 Deprecate CKT in favor of qiskit-addon-cutting by @garrison in https://github.com/Qiskit/qiskit-addon-cutting/pull/663 Prepare 0.8.0 release by @garrison in https://github.com/Qiskit/qiskit-addon-cutting/pull/664 Full Changelog: https://github.com/Qiskit/qiskit-addon-cutting/compare/0.7.3...0.8.0 <!-- published by ghalactic/github-release-from-tag -->
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.126 | 0.125 |
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; both teacher heads agree on what is shown here.
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".