An Approximate Parallel Annealing Ising Machine for Solving Traveling Salesman Problems
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Bibliographic record
Abstract
Annealing-based Ising machines have emerged as high-performance solvers for combinatorial optimization problems (COPs). As a typical COP with constraints imposed on the solution, traveling salesman problems (TSPs) are difficult to solve using conventional methods. To address this challenge, we design an approximate parallel annealing Ising machine (APAIM) based on an improved parallel annealing algorithm. In this design, adders are reused in the local field accumulator units (LAUs) with half-precision floating-point representation of the coefficients in the Ising model. The momentum scaling factor is approximated by a linear, incremental function to save hardware. To improve the solution quality, a buffer-based energy calculation unit selects the best solution among the found candidate results in multiple iterations. Finally, approximate adders are applied in the design for improving the speed of accumulation in the LAUs. The design and synthesis of a 64-spin APAIM show the potential of this methodology in efficiently solving complicated constrained COPs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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