OptiPauli: An algorithm to find a near-optimal Pauli Feature Map for Quantum Support Vector Classifiers
Bibliographic record
Abstract
Quantum Support Vector Classifiers have been gaining traction in solving classification problems as it enables efficient kernel estimation and potential improvement in accuracy. To estimate the kernel matrices, a quantum kernel needs a Pauli Feature Map which encodes the classical data into the quantum state space. For a given dataset and dimension, we can have an exorbitant number of Pauli Feature Maps. Hence, finding a Pauli Feature Map that maximizes model accuracy is a research challenge. To address this optimization problem, we propose an algorithm that finds a near-optimal Pauli Feature Map by solving several sub-problems with varying numbers of decision variables and their constraints. Each sub-problem aims to find the Pauli Feature Map that maximizes the model accuracy. We use genetic algorithm to solve each sub-problem, and then select the best out of all the near-optimal Pauli Feature Maps. Instead of formulating a single optimization problem, we use this divide-and-conquer approach (of breaking down the problem into multiple sub-problems) to reduce the overall search space. To evaluate the efficiency of our algorithm, we compare it against solving each sub-problem through exhaustive approach. The latter yields the optimal Pauli Feature Map by exploring the full state space. For the scikit-learn Breast Cancer dataset with dimension 5, our algorithm converges to the optimal Pauli Feature Map in about 804 seconds which is over 4 times faster than the exhaustive approach.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".