Reversible Logic Synthesis with Output Permutation
Why this work is in the frame
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Bibliographic record
Abstract
Synthesis of reversible logic has become a very important research area. In recent years several algorithms--heuristic as well as exact ones--have been introduced in this area. Typically, they use the specification of a reversible function in terms of a truth table as input. Here, the position of the outputs are fixed. However, in general it is irrelevant, how the respective outputs are ordered. Thus, a synthesis methodology is proposed that determines for a given reversible function an equivalent circuit realization modulo output permutation. More precisely, the result of the synthesis process is a circuit realization whose output functions have been permuted in comparison to the original specification and the respective permutation vector. We show that this synthesis methodology may lead to significant smaller realizations. We apply Synthesis with Output Permutation (SWOP) to both, an exact and a heuristic synthesis algorithm. As our experiments show using the new synthesis paradigm leads to multiple control Toffoli networks that are smaller than the currently best known realizations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it