Is Circuit Depth Accurate for Comparing Quantum Circuit Runtimes?
Bibliographic record
Abstract
Although quantum circuit depth is commonly used to approximate circuit runtimes, it overlooks a prevailing trait of current hardware implementation: different gates have different execution times. Recognizing the potential for discrepancies, we investigate depth's accuracy for comparing runtimes between compiled versions of the same circuit. In particular, we assess the accuracy of traditional and multi-qubit depth for (1) predicting relative differences in runtime and (2) identifying compiled circuit version(s) with the shortest runtime. Finding that circuit depth is not accurate for either task, we introduce a new metric, gateaware depth, that weights gates' contributions to runtime using an architecture's average gate execution times. Using average gate times allows gate-aware depth to capture variations by gate type without requiring exact knowledge of all gate times, increasing accuracy while maintaining portability across devices of the same architecture. Compared to traditional and multiqubit depth, gate-aware depth reduces the average relative error of predictions in task (1) by 68 and 18 times and increases the average number of correct identifications in task (2) by 20 and 43 percentage points, respectively. Finally, we provide gate-aware depth weight configurations for current IBM Eagle and Heron architectures.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".