Optimality and annealing path planning of dynamical analog solvers

TL;DR

This paper develops a theoretical framework to analyze and improve the dynamics of analog Ising solvers, demonstrating rapid convergence and optimized parameter schedules for solving complex spin glass models.

Contribution

It introduces a dynamical mean-field approach to understand Ising machine behavior and proposes optimized annealing schedules to enhance their practical performance.

Findings

Solutions typically reached within constant time complexity

Temperature-only annealing benefits observed in Coherent Ising Machine

Framework improves Ising machine effectiveness for complex problems

Abstract

Recently proposed analog solvers based on dynamical systems, such as Ising machines, are promising platforms for large-scale combinatorial optimization. Yet, given the heuristic nature of the field, there is very limited insight on optimality guarantees of the solvers, as well as how parameter schedules shape dynamics and outcomes. Here, we develop a dynamical mean-field framework to analyze Ising-machine dynamics for finding the ground state energy of the Sherrington-Kirkpatrick(SK) model of spin glasses and identify mechanisms that enable rapid convergence to provenly near-optimal energies. For a fixed target energy density Ec, we show that solutions are typically reached within O(1) matrix vector multiplications, indicating constant time complexity. We further delineate theoretical limitations arising from different parameter-scheduling trajectories and demonstrate a pronounced benefit of temperature-only annealing for the Coherent Ising Machine. Building on these insights, we propose a general framework for designing optimized parameter schedules, thereby improving the practical effectiveness of Ising machines for complex optimization tasks. The superior performance of the dynamical solvers is illustrated by the attainment of the ground state energy of the SK model.