Explainable Quantum Machine Learning Through Interpretation of Variational Circuit Outcomes in Classification Tasks
Bibliographic record
Abstract
This study explores explainability approaches that are independent of models, with a particular emphasis on Integrated Gradients (IG) and Baseline SHAP (BS) within the framework of parametrized quantum circuits (PQCs). After a thorough mathematical examination, we modify BS to a new Polynomial SHAP (qSHAP) approach optimized for PQCs; this allows us to estimate SHAP values efficiently using rankone polynomial approximations. We prove that qSHAP is resilient even in quantum contexts with noise by evaluating the stability of IG and BS in the presence of quantum noise via the central limit theorem. Tests on the Bars and Stripes dataset with one-, two-, and four-qubit classifiers confirm that our method works in different environments, such as classical simulations, noise models, and actual quantum computers. The results demonstrate that qSHAP offers consistent and dependable feature attributions, whereas IG's performance drops with noise. This suggests that qSHAP might be a useful tool for explainability in the near future of quantum machine learning, resistant to noise.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".