Better than classical? The subtle art of benchmarking quantum machine\n learning models
Bibliographic record
Abstract
Benchmarking models via classical simulations is one of the main ways to\njudge ideas in quantum machine learning before noise-free hardware is\navailable. However, the huge impact of the experimental design on the results,\nthe small scales within reach today, as well as narratives influenced by the\ncommercialisation of quantum technologies make it difficult to gain robust\ninsights. To facilitate better decision-making we develop an open-source\npackage based on the PennyLane software framework and use it to conduct a\nlarge-scale study that systematically tests 12 popular quantum machine learning\nmodels on 6 binary classification tasks used to create 160 individual datasets.\nWe find that overall, out-of-the-box classical machine learning models\noutperform the quantum classifiers. Moreover, removing entanglement from a\nquantum model often results in as good or better performance, suggesting that\n"quantumness" may not be the crucial ingredient for the small learning tasks\nconsidered here. Our benchmarks also unlock investigations beyond simplistic\nleaderboard comparisons, and we identify five important questions for quantum\nmodel design that follow from our results.\n
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".