Context-aware fidelity estimation
Bibliographic record
Abstract
We present context-aware fidelity estimation (CAFE), a framework for benchmarking quantum operations that offers several practical advantages over existing methods such as randomized benchmarking (RB) and cross-entropy benchmarking. In CAFE, a gate or a subcircuit from some target experiment is repeated $n$ times before being measured. By using a subcircuit, we account for effects from the spatial and temporal circuit context. Since coherent errors accumulate quadratically while incoherent errors grow linearly, we can separate them by fitting the measured fidelity as a function of $n$. One can additionally interleave the subcircuit with dynamical decoupling sequences to remove certain coherent error sources from the characterization when desired. We have used CAFE to experimentally validate our single- and two-qubit unitary characterizations by measuring fidelity against estimated unitaries. In numerical simulations, we find that CAFE produces fidelity estimates at least as accurate as interleaved RB while using significantly fewer resources. We also introduce a compact formulation for preparing an arbitrary two-qubit state with a single entangling operation and use it to present a concrete example using CAFE to study controlled-$Z$ gates in parallel on a Sycamore processor.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".