Abstract:We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization problems, including Quadratic Unconstrained Binary Optimization (QUBO), binary sparse coding, and planted-solution Ising models. By parameterizing discrete binary variables as continuous wave-like states on the complex unit circle, we inherently smooth highly non-convex energy landscapes. We show that representing binary variables as complex phases reveals an implicit regularization mechanism that promotes convergence toward discrete states. Extracting this mechanism yields significant improvements even within standard real-valued optimization frameworks, using this regularizer explicitly. Empirically, this regularization yields vastly higher ground-state convergence rates than standard real-valued alternatives. Our models achieved zero error in large-scale 160x160 QUBO tasks under severe noise (sigma=0.25), and outperformed traditional algorithms (OMP and LASSO) in underdefined sparse coding with perfect recovery at sigma=0.15. The solver's robustness was further validated by recovering exact ground-state configurations in 8 out of 11 rigorously engineered planted-solution benchmarks.
| Comments: | 27 pages, 5 figures |
| Subjects: | Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Combinatorics (math.CO); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2605.24502 [cond-mat.stat-mech] |
| (or arXiv:2605.24502v1 [cond-mat.stat-mech] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24502 arXiv-issued DOI via DataCite (pending registration) |
From: Khen Cohen [view email]
[v1]
Sat, 23 May 2026 10:18:58 UTC (1,608 KB)
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