benchmarks evals
The Circuit Description Explained Most of the Mitigation Gain
Capacity-matched controls reproduced 87.7–100.5% of selected learners' gain in familiar simulated regimes.
Summary
Capacity-matched controls reproduced 87.7–100.5% of selected learners' gain in familiar simulated regimes.
Learned quantum error mitigation can appear successful even when a model mostly reads circuit structure rather than the noisy measurement. QEMScore pairs each mitigator with an equally flexible control that never sees the measurement. Across two simulated spin-chain families and three seeds, those controls matched 87.7% to 100.5% of the selected mitigators' gain over an affine descriptor fit; a polynomial descriptor model beat the mitigator in all six evaluations. Released Q-LEAR and QRAFT hardware data differed, with measurement inputs adding predictive value. The paper argues that mitigation results need capacity-matched no-measurement controls.
Why it matters
Capacity-matched controls reproduced 87.7–100.5% of selected learners' gain in familiar simulated regimes.
Limits and context
No additional limitation was separately recorded.
Key claims
Capacity-matched controls reproduced 87.7–100.5% of selected learners' gain in familiar simulated regimes.
Evidence: source-2026-09-17-013
Sources
- arXiv preprint 2609.17896arXiv · primary research
Corrections
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