Comparing and Integrating Constraint Programming and Temporal Planning\n for Quantum Circuit Compilation
Bibliographic record
Abstract
Recently, the makespan-minimization problem of compiling a general class of\nquantum algorithms into near-term quantum processors has been introduced to the\nAI community. The research demonstrated that temporal planning is a strong\napproach for a class of quantum circuit compilation (QCC) problems. In this\npaper, we explore the use of constraint programming (CP) as an alternative and\ncomplementary approach to temporal planning. We extend previous work by\nintroducing two new problem variations that incorporate important\ncharacteristics identified by the quantum computing community. We apply\ntemporal planning and CP to the baseline and extended QCC problems as both\nstand-alone and hybrid approaches. Our hybrid methods use solutions found by\ntemporal planning to warm start CP, leveraging the ability of the former to\nfind satisficing solutions to problems with a high degree of task optionality,\nan area that CP typically struggles with. The CP model, benefiting from\ninferred bounds on planning horizon length and task counts provided by the warm\nstart, is then used to find higher quality solutions. Our empirical evaluation\nindicates that while stand-alone CP is only competitive for the smallest\nproblems, CP in our hybridization with temporal planning out-performs\nstand-alone temporal planning in the majority of problem classes.\n
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".