În afara temei aiqbench
Această știre a fost preluată de la o sursă pe care o urmărim, dar nu ține de domeniul AI sau cuantic, așa că nu există o perspectivă aiqbench pentru ea.
Competitive scaling of small-subsystem-optimization heuristics for LABS
arXiv:2610.14361v1 Announce Type: cross Abstract: The low autocorrelation binary sequences (LABS) problem, often formulated as a spin-lattice energy-minimization problem, is a combinatorial optimization problem with a very rugged energy landscape. We demonstrate that iterative optimization of very small subsystems of n spins that are decoupled from the rest of the system achieves global optimization of LABS with a time-to-solution scaling that is competitive with heuristics that optimize the global system of N spins directly. Our isolated-subsystem heuristic with n = 4 achieves a time scaling of O(1.333N ) on the interval N = [27, 60], while the current state-of-the-art classical and quantum scalings are respectively O(1.34N ) and O(1.21N ). Our results suggest that specialized optimization hardware with only small numbers of logical spins may be of practical relevance.
Abstract:The low autocorrelation binary sequences (LABS) problem, often formulated as a spin-lattice energy-minimization problem, is a combinatorial optimization problem with a very rugged energy landscape. We demonstrate that iterative optimization of very small subsystems of n spins that are decoupled from the rest of the system achieves global optimization of LABS with a time-to-solution scaling that is competitive with heuristics that optimize the global system of N spins directly. Our isolated-subsystem heuristic with n = 4 achieves a time scaling of O(1.333^N ) on the interval N = [27, 60], while the current state-of-the-art classical and quantum scalings are respectively O(1.34^N ) and O(1.21^N ). Our results suggest that specialized optimization hardware with only small numbers of logical spins may be of practical relevance.
Statistical Mechanics (cond-mat.stat-mech); Quantum Physics (quant-ph)
arXiv:2610.14361 [cond-mat.stat-mech]
(or arXiv:2610.14361v2 [cond-mat.stat-mech] for this version)
https://doi.org/10.48550/arXiv.2610.14361 Focus to learn more arXiv-issued DOI via DataCite
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