Scaling advantage of chaotic amplitude control for high-performance combinatorial optimization

Finding the ground state of a variety of complex systems can be formulated as the minimization of the total interaction energy of Ising machines, posing a challenge as computational cost increases exponentially with system size. In this paper, the authors propose an algorithm to find the ground states of Ising-type problems by destabilising non-trivial attractors in combinatorial optimisation solvers through a heuristic modulation of the target amplitude, and show that this provides an improved scaling with respect to several existing methods.