Edge-of-chaos enhanced quantum-inspired algorithm for combinatorial optimization

TL;DR

This paper introduces a generalized quantum-inspired algorithm that leverages the edge of chaos in nonlinear dynamical systems to significantly improve combinatorial optimization performance, achieving near-perfect success rates on large problems.

Contribution

The authors generalize the simulated bifurcation algorithm with nonlinear control, demonstrating ultrafast solutions and high success probabilities by exploiting chaos at the edge of chaos.

Findings

Achieved nearly 100% success probability for large-scale problems.

Reduced solution time for 2000-variable problems to 10 ms, two orders of magnitude faster.

Discovered that high performance occurs near the edge of chaos in the system.

Abstract

Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables. Numerical simulations of them are also useful as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner, 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 quantum-inspired 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 surprisingly high performance, namely, 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 for physics-inspired approaches to combinatorial optimization.