Harnessing the edge of chaos for combinatorial optimization

• URL: http://arxiv.org/abs/2508.17655v3
• Date: Mon, 01 Sep 2025 08:07:35 GMT
• Title: Harnessing the edge of chaos for combinatorial optimization
• Authors: Hayato Goto, Ryo Hidaka, Kosuke Tatsumura,
• Abstract summary: We show that simulated bifurcation (SB) can achieve almost 100% success probabilities for large-scale problems.<n>The time to solution for a 2,000-variable problem is shortened to 10 ms by a GSB-based machine.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables. In particular, numerical simulations of them can be used as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner using cutting-edge many-core processors, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accurate than conventional approaches with discrete variables such as simulated annealing. To improve the solution accuracy of a representative dynamical system-based algorithm called simulated bifurcation (SB), which was found from classical simulation of a quantum nonlinear oscillator network exhibiting quantum bifurcation, here we generalize it by introducing nonlinear control of individual bifurcation parameters and show that the generalized SB (GSB) can achieve almost 100% success probabilities for some large-scale problems. As a result, the time to solution for a 2,000-variable problem is shortened to 10 ms by a GSB-based machine, which is two orders of magnitude shorter than the best known value, 1.3 s, previously obtained by an SB-based machine. To examine the reason for the ultrahigh performance, we investigated chaos in the GSB changing the nonlinear-control strength and found that the dramatic increase of success probabilities happens near the edge of chaos. That is, the GSB can find a solution with high probability by harnessing the edge of chaos. This finding suggests that dynamical-system approaches to combinatorial optimization will be enhanced by harnessing the edge of chaos, opening a broad possibility to tackle intractable combinatorial optimization problems by nature-inspired approaches.

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