Scaling advantage of nonrelaxational dynamics for high-performance combinatorial optimization

TL;DR

This paper demonstrates that nonrelaxational dynamics, specifically chaotic amplitude control, can significantly outperform relaxational methods in sampling low energy states for combinatorial optimization, showing better scaling and reduced variance.

Contribution

It introduces a novel nonrelaxational dynamics approach called chaotic amplitude control and demonstrates its advantages over traditional relaxational methods in Ising machine implementations.

Findings

Nonrelaxational dynamics accelerate low energy state sampling.

Chaotic amplitude control exhibits better scaling with problem size.

Reduced variance in solution quality compared to relaxational schemes.

Abstract

The development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to drastically transform our ability to understand and control complex systems. However, most of the physical implementations of such machines have been based on a similar concept that is closely related to relaxational dynamics such as in simulated, mean-field, chaotic, and quantum annealing. We show that nonrelaxational dynamics that is associated with broken detailed balance and positive entropy production rate can accelerate the sampling of low energy states compared to that of conventional methods. By implementing such dynamics on field programmable gate array, we show that the nonrelaxational dynamics that we propose, called chaotic amplitude control, exhibits a scaling with problem size of the time to finding optimal solutions and its variance that is significantly smaller than that of relaxational schemes recently implemented on Ising machines.